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SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL
2024, VOL. 12, NO. 1, 2394429
https://doi.org/10.1080/21642583.2024.2394429
Fuzzy logic approach for controlling uncertain and nonlinear systems: a
comprehensive review of applications and advances
Hooi Hung Tang and Nur Syazreen Ahmad
School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia
ABSTRACT
This paper presents a comprehensive review of the latest developments in fuzzy logic (FL) applica-
tions across critical domains which include energy harvesting (EH), ambient conditioning systems
(ACS), and robotics and autonomous systems (RAS), highlighting FL’s capability to address nonlin-
earities and uncertainties in diverse technological environments. Through a detailed comparative
analysis of research trends over the past decade, it underscores the increasing significance of FL in
EH and RAS, contrasting with the sustained interest in ACS. Furthermore, the evaluation of differ-
ent fuzzy inference systems across domains provides valuable insights into their specific strengths
and limitations, aiding researchers and practitioners in making informed decisions aligned with their
application needs. Additionally, the paper explores advanced modifications and hybridizations of FL,
such as swarm intelligence and integrations with other control strategies, emphasizing the neces-
sity for robust and adaptive FL systems. The review also identifies key open problems and potential
research directions, such as the demand for adaptive FL systems in EH and advanced optimization
techniques in ACS and RAS. Overall, this state-of-the-art review not only summarizes the current
state of FL applications but also outlines a roadmap for future research, offering valuable insights
for advancing FL’s role in handling uncertainties and nonlinearities in complex systems.
ARTICLE HISTORY
Received 25 May 2024
Accepted 14 August 2024
KEYWORDS
Fuzzy logic; energy
harvesting; ambient
conditioning systems;
robotics and autonomous
system
1. Introduction
Addressing uncertainties and nonlinearities is paramount
in various technological domains where traditional app
roaches fall short. Uncertain systems exhibit unpre-
dictable behaviour due to factors such as variable inputs,
imperfect knowledge of system dynamics, or environ-
mental disturbances. Nonlinear systems are characterized
by relationships between inputs and outputs that do not
follow a linear or proportional relationship. In nonlinear
systems, small changes in inputs can lead to dispropor-
tional or unexpected changes in outputs. This complexity
often makes them difficult to model and control using tra-
ditional linear methods. In response to these challenges,
Fuzzy Logic (FL), introduced by Lotfi Zadeh in 1975, rep-
resents a paradigm shift from the binary precision of
traditional logic systems (Zadeh, 1975). FL’s emergence
within artificial intelligence (AI) as a subset of soft com-
puting marks a significant advancement in handling the
inherent ambiguity and uncertainty of real-world prob-
lems. Unlike conventional logic systems that require strict
precision, FL allows for approximate reasoning, mimick-
ing the nuanced decision-making processes of human
cognition (Y. Jiang et al., 2016).
CONTACT Nur Syazreen Ahmad syazreen@usm.my
The fundamental departure of FL from binary logic is
in its concept of degrees of truth, allowing variables to
express values continuously between 0 and 1, unlike the
rigid true or false constraints of traditional systems. This
flexibility is crucial for handling the vagueness and ambi-
guity that are characteristic of many scientific and engi-
neering disciplines. FL is operationalized through Fuzzy
Inference Systems (FIS), which employ rule-based frame-
works to process inputs and derive outputs. These sys-
tems are adept at navigating the complexities inherent
in a wide range of applications, making them indispens-
able in sectors as varied as automated control systems
and consumer electronics.
The architecture of FIS is adeptly designed to tackle the
complexities associated with uncertain and nonlinear sys-
tems, which are commonly encountered in diverse appli-
cation domains such as energy harvesting (EH), ambi-
ent conditioning systems (ACS), as well as robotics and
autonomous systems (RAS). In the context of EH, FL plays
a pivotal role in managing the unpredictable nature of
energy sources like solar and wind. By applying FL prin-
ciples, FIS can dynamically adjust control parameters to
optimize the efficiency of power conversion and stor-
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2H. H. TANG AND N. S. AHMAD
age despite fluctuating input conditions (Suganthi et al.,
2015). This adaptability is crucial for maximizing energy
yield and ensuring the reliability of energy systems in vari-
able environmental conditions (Bendib et al., 2014). In
ACS, which include heating, ventilation, and air condition-
ing (HVAC) systems and controlled greenhouse environ-
ment, FIS leverages its capacity to handle dynamic and
nonlinear environmental factors (Vanegas et al., 2022).
FL enables these systems to maintain optimal environ-
mental conditions through precise control over temper-
ature, humidity, and air quality, thus ensuring comfort
and energy efficiency. The ability to fine-tune responses
based on fuzzy assessments of the environmental inputs
helps in significantly reducing energy consumption while
improving the living or growth conditions within these
environments (Belman-Flores et al., 2022).
With regard to RAS, the challenges are particularly
pronounced due to the complex and nonlinear nature
of both their low-level and high-level control systems
(Jerković Stil et al., 2020; Saffiotti, 1997; Teo et al., 2020).
Traditional control methods often fall short in less pre-
dictable and nonlinear environments (Al-Odienat & Al-
Lawama, 2008; Bello et al., 2023; Gouda et al., 2000).
Here, FL’s capacity to handle imprecision and to model
complex relationships using linguistic variables offers a
robust alternative. FL’s approach is not only about man-
aging complexity but also about providing adaptable and
nuanced solutions that can significantly enhance system
performance and reliability in unpredictable conditions
(Ferdaus et al., 2020; Sepulveda et al., 2007). By enhancing
the adaptability and decision-making capabilities of RAS,
FL enables these systems to perform optimally in unstruc-
tured environments where rapid and reliable responses
to unforeseen circumstances are necessary (Rath et al.,
2018). In addition, implementing FL allows robots to inter-
pret a wide range of sensor data more effectively, leading
to improved navigation, obstacle avoidance, and inter-
action with humans and other machines (Pradhan et al.,
2009). This capacity for nuanced decision-making under
uncertainty makes FL indispensable for advanced control
and operation in robotics and autonomous systems (Tai
et al., 2016).
This review paper makes several significant contribu-
tions that distinguish it from existing literature. It pro-
vides a state-of-the-art review of FL applications across
the aforementioned critical domains, i.e. EH, ACS and RAS.
Unlike previous reviews that focus on specific applica-
tions or techniques, this paper offers a holistic view of FL’s
versatility in managing nonlinearities and uncertainties in
various technological contexts. It includes a detailed com-
parative analysis of research trends over the past decade,
highlighting the growing importance of FL in EH and RAS
compared to the stable interest in ACS. Additionally, the
evaluation of different FIS across different domains pro-
vides insights into their specific strengths and limitations,
helping researchers and practitioners make informed
decisions based on their application needs.
Furthermore, this paper addresses advanced modifica-
tions and hybridizations of FL, such as swarm intelligence
(SI), adaptive methods, and integrations with other con-
trol strategies such as proportional-integral-derivative
(PID) and sliding mode control (SMC), emphasizing the
need for robust and adaptive FL systems. It also identifies
several open problems and potential research directions,
such as the need for adaptive FL systems in EH, advanced
optimization techniques in ACS, and deep learning inte-
grations in RAS. By doing so, this review not only summa-
rizes the current state of FL applications but also provides
a roadmap for future research, offering valuable insights
for advancing FL’s role in handling uncertainties and non-
linearities in complex systems.
The remaining sections of the paper are organized as
follows; Section 2details the methodology employed to
screen relevant articles for in-depth review and analysis.
Section 3provides a brief overview of FL which includes
its basic architecture, FIS, and membership functions.
Section 4explores how FL is utilized to address uncertain-
ties and nonlinearities in EH, ACS and RAS. Section 5offers
a comparative analysis, highlighting research trends,
identifying gaps, and suggesting potential directions for
future research. Finally, Section 6concludes the paper.
2. Methodology
In order to search for relevant articles that focus on appli-
cations of FL to enhance system performance under var-
ious forms of perturbations within the fields of EH, ACS
and RAS, this work employs the PRISMA 2020 method
as suggested in Page et al. (2021). The PRISMA 2020
method, updated from the 2009 version, offers new
guidance on reporting, identifying, selecting, apprais-
ing, and synthesizing studies, improving the clarity and
usability of the review process. Scopus and Web of Sci-
ence were chosen as the databases for their exten-
sive, reliable, and multidisciplinary coverage of peer-
reviewed literature. Figure 1illustrates the methodology
based on PRISMA 2020 employed in this study. The key-
word ‘fuzzy logic’ was used to initially filter the articles,
with additional domain-specific keywords employed to
refine the search. For EH, keywords included ‘maximum
power point’, ‘photovoltaic’, and ‘wind turbine’; for ACS,
‘ambient conditioning’ and ‘air conditioning’ were used;
and for RAS, ‘robotic’, ‘autonomous’, and ‘robot’ were
selected. Furthermore, the publication year was restricted
to the recent decade, from 2014 to 2023, to ensure
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 3
Figure 1. Article screening process via PRISMA 2020 method.
the most current research was reviewed. This method-
ical approach ensures a thorough review and selec-
tion process, enabling the research to comprehensively
address the efficacy of FL in improving system resilience
against environmental and operational perturbations in
targeted application domains. By leveraging the PRISMA
2020 framework and focussing on recent publications,
this study establishes a robust foundation for exploring
innovative applications of FL, potentially leading to sig-
nificant advancements in technology and engineering
practices.
3. Fuzzy logic overview
3.1. Fuzzy logic architecture
FL operates on the foundation of rule-based systems,
articulated through constructs like ‘If X is A, then Y is
B’. Magdalena (2015). These rules, grounded in linguis-
tic variables, play a pivotal role in expressing knowl-
edge and facilitating decision-making. The structured
approach provided by these rules becomes particularly
valuable in scenarios where uncertainties prevail, offer-
ing a means to navigate the complexities inherent in
real-world situations (Mendel, 2017).
The diagram in Figure 2illustrates the basic structure
of a Fuzzy Inference System (FIS), a framework for reason-
ing with Fuzzy Logic (FL). The system comprises several
key components. First, the fuzzification process converts
crisp input values into fuzzy sets using membership func-
tions, representing the degree to which the input values
belong to each defined fuzzy set. The inference engine,
the core component where reasoning occurs, uses these
fuzzy input sets and applies rules from the rule base to
generate fuzzy output sets. These rules are typically for-
matted as ‘IF-THEN’ statements. The rule base contains a
collection of fuzzy rules defining the system’s behaviour,
linking conditions based on fuzzy input sets to conse-
quences as fuzzy output sets. Membership functions play
a crucial role, mapping each point in the input space to a
membership value between 0 and 1 during both fuzzifi-
cation and defuzzification processes. Finally, the defuzzi-
fication step converts the fuzzy output sets back into crisp
output values, using various methods such as the cen-
troid method, which computes the centre of gravity of the
fuzzy output set.
3.2. Fuzzy inference system (FIS)
There are two prevalent styles of FIS, namely Mamdani
and Takagi-Sugeno (T-S). The Mamdani inference system,
proposed by Ebrahim Mamdani in 1975 (Mamdani, 1974),
is one of the most commonly used FIS. It is characterized
by its use of FL principles for both the antecedent (IF part)
and the consequent (THEN part) of the rules. Its fuzzy rules
operate on fuzzy sets within the input space to yield fuzzy
sets in the output space. The T-S inference system, devel-
oped by Takagi and Sugeno in 1985 (Takagi & Sugeno,
1985), differs from the Mamdani system in how the con-
sequent (THEN part) is handled. In T-S systems, the output
is a mathematical function (a constant or linear function)
of the input variables, rather than a fuzzy set.
One key advantage of the Mamdani systems lies in
its linguistic interpretability, as it articulates rules in a
format understandable to humans, fostering ease of
comprehension and validation. Additionally, it excels in
non-linear mapping, effectively capturing complex rela-
tionships between inputs and outputs through fuzzy
rules and membership functions. However, these bene-
fits come with trade-offs. The Mamdani systems can be
computationally intensive, particularly when handling a
plethora of fuzzy rules and intricate membership func-
tions. Furthermore, the process of output defuzzification,
which involves converting fuzzy output sets into crisp
values, can occasionally introduce ambiguities or inaccu-
racies, potentially undermining system performance.
The T-S systems on the other hand offer simplified
computation compared to the Mamdani systems, as they
bypass the generation of fuzzy outputs, leading to faster
execution and reduced computational overhead. More-
over, their mathematical simplicity renders them con-
ducive to data-driven learning approaches, such as arti-
ficial neural network (ANN) training or parameter esti-
mation techniques, enhancing efficiency in Learning.
Nonetheless, these advantages are offset by certain draw-
backs. T-S systems sacrifice some level of interpretability,
4H. H. TANG AND N. S. AHMAD
Figure 2. The basic fuzzy logic architecture.
Tab le 1 . Comparison of Mamdani and T-S inference systems.
Fuzzy Inference System
Aspect Mamdani Takagi-Sugeno (T-S)
Fuzzification Convert crisp inputs into fuzzy sets using membership functions.
Rule evaluation The fuzzy rules are applied from the rule base to the fuzzy
input sets. This involves evaluating the degree of match
between the fuzzy input and the rule antecedents, typically
using the minimum or product operators.
The fuzzy rules are applied from the rule base to the fuzzy
input sets. Each rule’s consequent is a function (typically
linear or constant) of the input variables.
Aggregation The fuzzy outputs of all rules are combined into a single
fuzzy set using operators like maximum or sum.
The outputs of all rules are combined, which are weighted
by their respective degrees of match. This usually involves
taking a weighted average of the rule outputs.
Defuzzification The aggregated fuzzy output set is converted into a crisp
output value. The most common method is the centroid
method.
The output is already a crisp value resulting from the
weighted average.
Advantages Linguistic interpretability Simplified computation
Non-linear mapping capability Efficiency in learning
Disadvantages Computational complexity Reduced interpretability
Output defuzzification ambiguity Limited expressiveness
lacking the linguistic transparency characteristic of Mam-
dani systems. The direct mapping from inputs to out-
puts may pose challenges in interpreting the rationale
behind the system’s decisions. Additionally, their inher-
ent assumption of linearity between inputs and outputs
can constrain their expressiveness, potentially limiting
their ability to capture intricate relationships as effectively
as Mamdani systems.
In summary, Mamdani and T-S inference systems
exhibit distinctive strengths and weaknesses. While Mam-
dani systems excel in linguistic interpretability and non-
linear relationship capture, they may encounter com-
putational complexity issues. Conversely, T-S systems
offer computational efficiency and learning efficacy but
may compromise interpretability and struggle with com-
plex relationship representation. The choice between
the two hinges on specific application requirements,
including the need for interpretability, computational
efficiency, and the complexity of input-output relation-
ships. Table 1summarizes the similarities and differences
between Mamdani and T-S inference systems, along with
their respective advantages and limitations.
3.3. Fuzzy membership functions
The FL membership functions are critical components
of FIS as they determine how input values are mapped
to degrees of membership in fuzzy sets. Common types
of membership functions include triangular, trapezoidal,
Gaussian, and sigmoidal functions. Designing these func-
tions typically relies on the heuristic method, which
depends on user experience or expertize. Experts set the
shapes, ranges, and parameters of the membership func-
tions based on their understanding of the system and
the specific application needs. This heuristic approach
ensures that the fuzzy system accurately captures the
nuances of the input data and the behaviour of the
system.
However, the heuristic method’s dependence on
expert knowledge can be a limitation, as it may not
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 5
always guarantee optimal performance across different
scenarios and applications. This challenge has motivated
researchers to explore automated methods for design-
ing and optimizing membership functions. Techniques
such as genetic algorithm (GA), particle swarm optimiza-
tion (PSO), and other machine learning approaches aim to
automate the search for optimal membership functions
(Mehdi Zangeneh & Forouzanfar, 2022). These methods
seek to enhance the efficiency and accuracy of fuzzy
systems by systematically identifying the best member-
ship functions that minimize error and improve system
response (Chao et al., 2017).
3.4. FL approach for handling uncertainties and
nonlinearities
To address uncertainties and nonlinearities in systems, FL
offers a robust approach through its distinctive method-
ologies:
•Handling Uncertainty: FL excels in managing uncer-
tainty and imprecision in system dynamics by utiliz-
ing linguistic variables and fuzzy rules. This approach
allows FL to effectively capture and process uncertain
inputs from environmental factors in real-time, ensur-
ing robust control even in varying conditions. This
capability is particularly beneficial in applications like
ACS, where precise control over environmental vari-
ables such as temperature, humidity, and air quality is
crucial for maintaining optimal indoor conditions.
•Nonlinear Mapping: FL can approximate complex non-
linear relationships between inputs and outputs with-
out explicit mathematical models. This capability is
crucial in applications such as Maximum Power Point
Tracking (MPPT) for EH. In ACS, where non-linear rela-
tionships between external environmental factors and
internal conditions exist, FL’s ability to handle such
complexities aids in maintaining stable and comfort-
able indoor environments.
•Adaptability: FL controllers are adaptive to changes in
system and environmental conditions, making them
suitable for dynamic systems like EH from renewable
sources and autonomous robotics. This adaptability
ensures efficient performance optimization despite
fluctuations in environmental variables. In ACS, FL’s
adaptive nature allows for responsive adjustments to
environmental changes, ensuring continuous comfort
and energy efficiency.
These attributes make FL particularly suitable for appli-
cations where traditional control methods struggle with
the inherent uncertainties and nonlinearities of real-
world systems.
4. Fuzzy logic applications on uncertain and
nonlinear systems
4.1. Energy harvesting (EH)
Over recent decades, the quest for clean and renew-
able energy sources has intensified significantly, driven
by escalating environmental concerns. Among these sus-
tainable options, technologies like wind turbines (WT)
and photovoltaic (PV) systems are anticipated to supply
an increasingly larger share of the world’s energy needs.
These advancements reflect a paradigm shift towards
more eco-friendly and sustainable energy solutions in
response to the pressing imperative of climate change
mitigation (H. Attia & Gonzalo, 2019).
4.1.1. Photovoltaic (PV)
The inherent randomness in energy harvesting, espe-
cially in PV systems, arises from environmental factors like
sunlight intensity, temperature fluctuations, and shading
effects. These factors introduce uncertainties into system
dynamics, leading to unpredictable variations in the out-
put power of PV panels. This unpredictability poses sig-
nificant challenges in accurately forecasting system per-
formance across different environmental conditions, due
to the stochastic nature of these factors. In addition, the
relationship between the input (solar irradiance and tem-
perature) and the output (PV panel voltage and current)
of a PV system is nonlinear. This nonlinearity arises due to
the physical characteristics of the semiconductor materi-
als used in PV cells. Under each environmental condition,
there exists a unique Maximum Power Point (MPP).
FL controllers (FLCs) are well-suited for MPPT in PV sys-
tems for several reasons. Firstly, they handle uncertainty
and system dynamics effectively using linguistic vari-
ables and fuzzy rules, ensuring robust control under vary-
ing conditions (Derbeli et al., 2023). Secondly, FLCs can
approximate complex nonlinear relationships between
inputs and outputs without needing detailed mathemat-
ical models, which is crucial for adapting to the dynamic
conditions of solar irradiance and temperature in MPPT
applications (Nasri et al., 2019). Lastly, FLCs adapt well to
changes in system and environmental parameters with-
out requiring extensive recalibration, making them ideal
for optimizing energy harvesting in PV systems with fluc-
tuating environmental conditions (Subramanian et al.,
2021).
Figure 3illustrates a standard FL-based MPPT control
for a PV system where the outputs of the PV are Vpv
and Ipv, which represent voltage and current respectively.
The FL-based MPPT control method is considered model-
free as it operates without requiring knowledge of the
system model. The inputs to the controller are typically
the system error, denoted as E, and the change in error,
6H. H. TANG AND N. S. AHMAD
Figure 3. Illustration of a standard FL-based MPPT control for a PV system. The load or other devices block is typically a battery or an
electrical grid.
represented as CE which are mathematically described as
follows (Yilmaz et al., 2018):
E(k)=Ppv
Vpv
=Ppv(k)−Ppv (k−1)
Vpv(k)−Vpv (k−1);
CE(k)=E(k)−E(k−1)(1)
where Ppv refers to the power. The output of the FLC
is a duty cycle, δ∈[0, 1], which is proportional to pulse
width modulation (PWM). In several other studies, the
variations in power, Ppv, and voltage, Vpv,areused
as inputs to the FLC (Cheng et al., 2015). The use of a
DC-DC boost power converter enables a rapid dynamic
response and helps stabilize DC voltage fluctuations. The
voltage conversion ratio of the boost converter can be
expressed as:
Vout
Vpv
=1
1−δ(2)
where Vout is the output voltage. Assuming a 100% con-
version efficiency for the boost converter, the relationship
between the input current, Iin, and the output current, Iout
can be described as follows:
Iout
Ipv
=I−δ(3)
Based on (2)–(3), when δchanges, the relationship
between the input impedance and the output load of the
boost converter can be represented by:
Rin =Vpv
Ipv
=(1−δ)2RL(4)
where RLdenotes the load. By employing this voltage
control and implementing PWM signal-based impedance
matching, it is possible to reach the MPP, thereby max-
imizing the harvesting of available PV power through
resistance adjustment.
Several studies have focussed on integrating FL with
traditional MPPT methods such as Perturb & Observe
(P&O) to enhance PV system performance across vari-
ous environmental conditions (Boukezata et al., 2016;El
Filali et al., 2016; Radjai et al., 2015). Meanwhile, numer-
ous other research efforts have employed P&O as a stan-
dard comparison to propose innovative FL-based MPPT
approaches (Giurgi et al., 2022; A. K. Pandey et al., 2023;
Verma, 2019). The study in Bounechba et al. (2014)for
instance demonstrates that a FL-based MPPT controller
for PV systems significantly enhances energy production
efficiency compared to P&O methods under variable tem-
perature and insolation conditions. SI techniques such as
GA(M.N.Alietal.,2021; C.-L. Liu et al., 2014; Yahiaoui et al.,
2023), PSO (Cheng et al., 2015), teaching learning based
optimization (TLBO), firefly algorithm (FFA) (Farajdadian &
Hosseini, 2019), and hybrid grey wolf cuckoo search opti-
mization (GWO-CS) (Chauhan et al., 2022) have also been
introduced in the literature to refine the FL membership
function in order to improve transient time and track-
ing accuracy of the MPPT. In Hai et al. (2022), the farm-
land fertility optimization (FFO) algorithm is employed to
optimize the FL membership function to improve per-
formance in the case of uniform irradiance (UI) and par-
tial shading (PS). Figure 4demonstrates the transforma-
tion of the FL membership function from a symmetric
form, achieved through the heuristic method (a), to an
asymmetric shape (b) following optimization by PSO as
proposed in Cheng et al. (2015).
It is also worth noting that under partially shaded con-
ditions, the power-voltage characteristic curve of solar PV
arrays exhibits multiple peaks, making traditional fuzzy
control algorithms for MPPT prone to settling at local
optima. To address this, an enhanced MPPT algorithm
combining FLC with differential flatness control theory is
introduced in Zou et al. (2020), where differential flatness
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 7
Figure 4. Transformation of the FL membership function from a symmetric form, achieved through the heuristic method (a), to an asym-
metric shape (b) following optimization by PSO as proposed in Cheng et al. (2015). (a) Membership function based on heuristic method
and (b) PSO-optimized membership function.
linearly increases the PV array’s output voltage, Vpv,to
locate the MPP voltage, Vmpp, subsequently stabilized by
the fuzzy control. The the effectiveness of such a method
is validated through MATLAB simulations and hardware
experiments.
The study in Zaghba et al. (2024) combines FL and
SMC to enhance the tracking efficiency, stability, and
responsiveness to dynamic changes in grid-connected
PV systems, particularly under the influence of varying
atmospheric conditions. Tang et al. (2017) introduced
fractional order FLC (FOFLC) that fine-tunes the fuzzy
domain through a reduction in the alpha factor as it nears
the MPP, effectively minimizing oscillations at the MPP.
As a result, this FOFLC approach enables rapid dynamic
responses to environmental changes and exhibits high
accuracy in tracking the MPP. An improved energy con-
version efficiency is demonstrated in Kanagaraj et al.
(2020) which employed a variable FOFLC (VFOFLC) where
the alpha term will expand or contract the input domain
of the FLC to shorten the tracking time and maintain a
steady-state output around the MPP. The work in Kan-
demir et al. (2018) proposed a reduced-rule compressed
FLC (RR-FLC) on a 150-W PV panel system model where
the simulation results demonstrated improved response
rate and tracking accuracy by 4.66% under standard test
conditions. To mitigate the negative impact of PS on PV
systems, Verma et al. (2020) developed an asymmetrical
interval type-2 FLC (IT-2 AFLC) based MPPT algorithm.
This algorithm is specifically tailored to locate the global
peak under PS conditions (PSC) across various PV array
configurations. In Boubekri et al. (2022), the focus is
on enhancing the performance of DC-DC Boost Con-
verter circuits used to power brushless DC (BLDC) motors
with solar PV panels, aiming to address voltage fluctu-
ations and low output issues. By integrating a PID con-
troller with the Firefly algorithm (FA) for optimization, the
study successfully improved the transient response of the
circuit.
Managing a power system that has two connected
areas, each with its own central solar power plant, is
challenging, especially when the system’s behaviour is
not straightforward. Load Frequency Controllers (LFCs),
which help maintain the system’s stability, face difficul-
ties under these conditions. The study in Shafei et al.
(2022) introduces an FLC that is fine-tuned using PSO
to replace the usual controller for primary frequency
control. Another approach in Soliman et al. (2023)sug-
gests using an enhanced version of this FLC, known as
interval type-2 FLC (IT2FLC), also optimized with PSO,
to serve as a reliable backup LFC. Both controllers use
the area control error (ACE) and its variation (ACE)
as inputs to reduce fluctuations in the power system’s
frequency. These advanced controllers show promise
in improving the efficiency of MPPT even when faced
with significant changes in demand and solar energy
levels.
8H. H. TANG AND N. S. AHMAD
Figure 5. Illustration of a standard FL-based MPPT control scheme for a WT system.
4.1.2. Wind turbine (WT)
WT systems are inherently nonlinear and face uncertain-
ties stemming from variable wind speeds and directions.
Additionally, the blade pitch angle plays a crucial role
in affecting their performance. Just as with PV systems,
FLC is well-suited to addressing these perturbations. FLC
thrives without the need for exact inputs and is capable
of managing the inherent variability and imprecision of
wind behaviour. To harvest energy through WTs, the con-
trol strategy is typically designed to modulate the rotor
speed to capture the maximum energy from the wind.
FLC can efficiently handle MPPT by continuously adjust-
ing the control variables to keep the turbine operating at
its optimal point despite fluctuating wind conditions.
A WT mainly has four parts: the blades that catch the
wind, a gearbox and shaft, a generator which is typically a
permanent magnet synchronous generator (PMSG), and
power converters. The blades convert the kinetic energy
of the wind into mechanical energy, which the generator
then turns into electricity. This electricity gets changed by
the power converters so it can be used by the electricity
grid.
The power extracted from the wind can be modelled
by the equation:
Pe=1
2ρAv3
wCp(λ,β) (5)
where Peis the power extracted, ρis the air density, Ais
the rotor area, vwis the wind speed, and Cpis the power
coefficient, which is dependent on the tip speed ratio (λ)
and the blade pitch angle (β). The tip speed ratio is given
by λ=ωrR
vw
where ωris the rotor speed, and Ris the rotor
radius.
Figure 5illustrates a standard FL-based MPPT control
scheme for a WT system. To achieve the maximum power
point (MPP) and thus optimize the power coefficient, Cp,it
is crucial to first set the blade pitch angle to its ideal value.
Subsequently, the rotor speed needs to be modified in
response to changes in wind speed in order to maintain
the tip speed ratio at its optimal level. The optimal rotor
speed can be determined using the following equation:
ωr,opt =λoptvw
R(6)
where λopt is the optimal tip speed ratio, vwis the wind
speed, and R, is the rotor radius. This formula ensures that
the turbine operates at its most efficient point under vary-
ing wind conditions. Nonetheless, measuring the actual
wind speed is challenging in practice. Therefore, an esti-
mation is typically made using the turbine’s aerodynamic
torque, Ta, and the rotor speed, ωr. Such a technique is
regarded as sensorless method.
The objective of the FL-based MPPT control is to adjust
λand βto maximize Cp, and hence Pe. The FLC will output
adjustments to βor the torque setting of the generator.
The controller will adjust the rotor speed to maintain an
optimal λthat maximizes Cpbased on current wind con-
ditions. Fuzzy rules can be designed to fine-tune these
parameters effectively despite the complex relationship
between them and the output power, which a traditional
controller might not handle well due to its dependency
on precise mathematical models.
Various FL-based MPPT control techniques have been
introduced in the literature. In J. Liu et al. (2017), the error,
ew, and change of error, eware used as inputs for the
FLC. The output from this controller is an optimal gen-
erator current, which then acts as a reference value for
a secondary feedback system that manages the genera-
tor current. The study in Yaakoubi et al. (2016)usesthe
change in electrical power, Peand the change in the
rotor speed, ωr, as inputs to the FLC, and the desired
rotor speed step change, ωr,opt, ad the output. Ochoa
et al. (2023) tackled power fluctuations in WTs by intro-
ducing a FL-based supercapacitor storage system paired
with time-constant fitting using an adaptive first-order
transfer function. In such a technique, Peand the super-
capacitor’s charge state (SOC) are used as inputs to the
FLC to generate the smoothing factor which produces
set-point values for the supercapacitor that can stabilize
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 9
the power to the grid. An adaptive FLC is proposed in
Zamzoum et al. (2020) to maintain power output at rated
levels and improve quality, while also reducing load on
the turbine and drive train under full load conditions by
adjusting the pitch angle, addressing the challenges of
power limitation and load reduction in large-scale WTs
operating at above-rated wind speeds. In Pehlivan et al.
(2022), the GA is employed to optimize the FL mem-
bership functions for controlling the pitch angle in WT
systems.
Salem et al. (2021) proposed an adaptive FLC gain
scheduling, which tracks the MPP with improved steady
and dynamic responses of the system under fluctuating
wind conditions. The proposed method achieves superior
tracking accuracy and efficiency compared to traditional
FLC and PI controllers, as evidenced by both simulation
and experimental testing. The study in Noureddine et al.
(2022) introduced a FL fractional order PI (FFOPI) control
where the membership function is optimized by PSO to
enhance transient response performance and robustness
of a PMSG-variable-speed WT system.
In addition, the double-fed induction generator (DFIG)
wind system has seen advancements through the devel-
opment of improved control algorithms for the rotor
side converter (RSC), specifically using a hybrid con-
trol strategy that integrates FL with SMC for precise
regulation of active and reactive powers (Belounis &
Labar, 2017; Mousavi et al., 2022). This approach has
minimized chattering, enhanced system performance in
speed monitoring and power regulation as demonstrated
in Zouggar et al. (2019) which used current error (ei)
and change of current error (ei) as the FL inputs. The
work in Palanimuthu et al. (2022) develops a fuzzy inte-
gral SMC (FISMC) for a DFIG-based WT system, utilizing
a T-S fuzzy approach to model the system’s nonlinear
behaviour and disturbances, enhancing stability with a
new fuzzy-based Lyapunov function under a membership
function-dependent H∞approach. Simulation results
show improved stability and a 10% better performance
index compared to traditional H∞methods.
A hybrid energy system combining PV panels and
WTsisintroducedinMahmoudietal.(2021). The study
evaluates the economic and reliability aspects of the
hybrid system with and without backup systems, using
a novel method that integrates a FLC with a harmony
search algorithm (HSA) to determine optimal system siz-
ing based on environmental data. The findings indicate
that incorporating a backup system can reduce costs by
up to 24%, and while increasing system reliability by 5%
raises costs by up to 36%, the utilization of fuzzy HSA
offers more favourable outcomes compared to the stan-
dard HSA.
4.1.3. Summary
Based on the discussions in previous sections, Table 2
summarizes the applications of FL to improve PV and
WT systems as reported in the literature from 2014 to
2023. The column labelled ‘App’. refes to the applica-
tion, ‘Modification’ indicates techniques used to optimize
or modify the FLCs, and ‘Advantages / Role of FL’ out-
lines the benefits and/or roles of the FL in the applica-
tion. ‘M’ and ‘T-S’ under the FIS category denote Mam-
dani and Takagi-Sugeno respectively. The term ‘heuristic’
under the ‘Modification’ category refers to techniques
where FL parameters, including membership functions,
are set based on expert knowledge, user experience,
or empirical data, and remain fixed unless manually
updated or reconfigured. The summary shows that FL
logic has been widely used to enhance the performance
of PV and WT systems by improving energy production
efficiency, tracking accuracy, robustness against distur-
bances, and overall system stability and reliability. Differ-
ent optimization techniques, such as GA, PSO, and others,
have been employed to further enhance the performance
of the FLCs.
4.2. Ambient conditioning systems (ACS)
FLC offers several advantages over traditional control
methods like PID controllers, particularly in ACS such as
HVAC and GH control systems, due to their highly non-
linear nature. The subsequent sections offer a review of
state-of-the-art FL approaches in managing these sys-
tems.
4.2.1. Heating, ventilation, and air conditioning
(HVAC)
HVAC systems are the primary consumers of energy in
buildings. As the demand for these systems grows, con-
trol designers are increasingly focussed on creating effi-
cient controllers that reduce energy consumption while
ensuring buildings remain comfortably heated or cooled.
FLCs are frequently selected for this purpose because
they can dynamically adjust heating and cooling outputs
in response to various factors, including outdoor temper-
ature, humidity, occupancy levels, and user preferences.
This adaptability allows for more effective and respon-
sive temperature management (Behrooz et al., 2018;Yau
& Chang, 2018).
A typical air conditioning (AC) system using a FLC is
illustrated in Figure 6, where eTdenotes the tempera-
ture error between the set room temperature and the
actual room temperature (Ta), and eHdenotes the humid-
ity error between the desired and the actual room humid-
ity (Ha). These two parameters are the primary inputs
10 H. H. TANG AND N. S. AHMAD
Tab le 2 . Summary of FL applications to improve energy harvesting systems, i.e. PV and WT, as reported in the literature from 2014 to 2023.
Ref Year App. FIS FL inputs FL outputs Modification Advantages / Role of FL
Bounechba et al. (2014) 2014 PV M E,CE duty cycle FL-P&O enhances energy production efficiency compared to P&O
under variable temperature and insolation conditions
Boukezata et al. (2016) 2016 PV M E,CE duty cycle FL-P&O robust tracking capability under rapidly increasing and
decreasing irradiance
El Filali et al. (2016) 2016 PV M E,CE duty cycle FL-P&O increases efficiency and robustness
C.-L. Liu et al. (2014) 2014 PV M Ppv,Vpv duty cycle GA, PSO improves transient time and tracking accuracy
Nasri et al. (2019) 2019 PV T-S E,CE duty cycle FL-H∞improves efficiency of PV systems under asymmetric satu-
ration of duty ratio
Cheng et al. (2015) 2015 PV M Ppv ,Vpv duty cycle PSO improves transient time and tracking accuracy
Radjai et al. (2015) 2015 PV M Ppv ,dP/dVpv duty cycle Heuristic modified P&O via FL to improve the dynamic response and
steady-state performance
Hai et al. (2022) 2022 PV M E,CE duty cycle FFO improve performance in the case of uniform irradiance and
PS
Zou et al. (2020) 2020 PV M Vpv −Vmpp change in duty cycle Heuristic stabilizes PV’s output voltage under PS conditions
Chauhan et al. (2022) 2022 PV M E,CE duty cycle GWO-CS improves efficiency and tracking accuracy
Yilmaz et al. (2018) 2022 PV M E,CE duty cycle Heuristic increased efficiency and reduced cost under variable tem-
perature and irradiance
M. N. Ali et al. (2021) 2021 PV M E,CE duty cycle GA, PSO improves transient time and tracking accuracy
Yahiaoui et al. (2023) 2023 PV M E,CE duty cycle GA improves efficiency and tracking accuracy
Farajdadian and Hosseini
(2019)
2019 PV M Ppv ,Vpv duty cycle TLBO, FFA improves convergence speed and tracking accuracy
Zaghba et al. (2024) 2024 PV M dPpv /dVpv duty cycle SMC improved tracking efficiency, stability, and responsiveness
to dynamic changes
Giurgi et al. (2022) 2022 PV M Ppv ,Vpv duty cycle Heuristic faster response to the variation of solar irradiance
Tang et al. (2017) 2017 PV M E,CE duty cycle FOFLC improves tracking accuracy in weather variations
Kandemir et al. (2018) 2018 PV M Ipv /Vpv ,dIpv /dVpv Vpv RR-FLC improves response rate and tracking accuracy
Subramanian et al. (2021) 2021 PV M E,CE duty cycle Heuristic improves system reliability and stability
Kanagaraj et al. (2020) 2020 PV M E,Ppv duty cycle VFOFLC improves Energy Conversion Efficiency
Verma (2019) 2019 PV M E,CE duty cycle AFLC improves steady state and dynamic behaviour of the PV
system under PS condition
Verma et al. (2020) 2020 PV M E,CE duty cycle IT-2 AFLC improves performance under PS condition
Boubekri et al. (2022) 2022 PV T-S E,CE duty cycle H∞, FA improves transient response of DC-DC boost converter cir-
cuits
A. K. Pandey et al. (2023) 2023 PV M E,CE duty cycle Heuristic more precise duty ratio change, identification of operating
point region, and faster MPP tracking
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 11
Derbeli et al. (2023) 2023 PV M Ppv ,Vpv duty cycle N/A improves steady-state oscillation, response time and over-
shoot values
Shafei et al. (2022) 2022 PV M ACE, ACE power PSO improves time response under severe load changes in two-
area multi-source interconnected power system
Soliman et al. (2023) 2023 PV M ACE, ACE power PSO-IT2FLC improves time response under severe demand load and
solar irradiance changes in two-area multi-source intercon-
nected power system
J. Liu et al. (2017) 2017 WT M ew,ewgenerator current’s ref-
erence
Heuristic sensorless, improves robustness against parameter varia-
tions compared to PI control
Yaakoubi et al. (2016) 2016 WT M Pe,ωrωr,opt Heuristic sensorless, improves robustness against wind speed varia-
tions
Ochoa et al. (2023) 2023 WT M Pe, SOC smoothing factor FL-based supercapacitor power smoothing in high-wind penetrated power systems
Zamzoum et al. (2020) 2020 WT M ew,ewgenerator current’s ref-
erence
Adaptive FLC improves speed, stability and robustness; higher electrical
energy production with lowest harmonics rate
Pehlivan et al. (2022) 2022 WT M Pe,CPe,ωrPitch angle reference GA Optimized pitch angle control,shor ter settling time and less
power fluctuation
Salem et al. (2021) 2021 WT M ew,ewelectromagnetic
torque step change
Adaptive FLC improves steady and dynamic responses of the system
under fluctuating wind conditions compared to traditional
FLC and PI controllers
Noureddine et al. (2022) 2022 WT M ew,ewgenerator current’s ref-
erence
FFOPI, PSO improves transient response performance and robustness
Belounis and Labar (2017) 2022 WT M ew,ewactive power’s
reference
FL-SMC reduces chattering due to SMC
Zouggar et al. (2019) 2019 WT M ei,eiAttractive control varia-
tion
FL-SMC reduces chattering due to SMC
Palanimuthu et al. (2022) 2022 WT T-S Stator output Inputs to SMC FISMC improves stability and a 10% better performance index
compared to traditional H∞methods
Mahmoudi et al. (2021) 2021 PV,WT M Iteration Pitch adjustment rate,
bandwitdh
HSA reduces costs, increased system reliability
12 H. H. TANG AND N. S. AHMAD
Figure 6. Illustration of a standard HVAC system with FLC.
to the FLC, which then determines the appropriate con-
trol actions to adjust the AC system. The study in Shah
et al. (2020) employs eTand eHto control the fan speed
(FS), chilled-water valve, and power. In Mohamed and
Mohamed (2015), four variables are employed as inputs
for the FLC, i.e. Ta,eT, dew temperature (Tdew), and elec-
tricity voltage (Ve). The FLC then produces outputs for
four control variables: FS, compressor speed (CS), mode of
operation, and fin direction. Abdo et al. (2018) enhanced
thermal comfort by incorporating six variables into the
FLC controller: eT, change in eT,(eT), static air pressure
difference, (eP), change in eP,(eP), differences in CO2
levels (eCO2), and change in eCO2(eCO2). These variables
are utilized to regulate the hot water valve, fan speed,
and fresh air dampers. Francis et al. (2022) designed an
energy-efficient, FL-based air conditioning system tai-
lored for Northeastern Nigeria. This system adjusts the
CS and operation mode by processing six input variables
to the FLC: Troom,eT,Tdew, number of occupants (NoC),
time of the day (ToD), and the prevailing weather condi-
tions. An adaptive FLC-based HVAC system is proposed
in Abuhussain et al. (2023) where the optimal setpoint
is determined using combinations of sensor input values
and price data from the smart grid. The FLC depends on
several variables which include Ta,Ha, NoC, initialized set-
point (Is), and energy price (Eprice).In Chojecki et al. (2023),
the heater/cooler power is controlled by a PI-structured
FLC where eTand eTserve as its inputs. In Rajeswari
Subramaniam et al. (2023), the study centres on manag-
ing thermal comfort and indoor air quality within a vehi-
cle’s HVAC system. The FLC utilizes the predicted mean
vote (PMV) index, CO2concentration, and Taas inputs to
regulate the FS, position of recirculation flaps, and the
refrigerant mass flow rate.
Another unique approach to provide a better temper-
ature prediction is via ANN, which is a computational
model inspired by the structure and function of bio-
logical neural networks (NNs), capable of learning and
performing tasks by processing data through intercon-
nected nodes arranged in layers (Arrouch, Mohamad-
Saleh, et al., 2022; Goay et al., 2019,2018). Such a method
is introduced in Collotta et al. (2014) which utilized an
ANN that forecasts Tato control the HVAC system’s on/off
switching and air inlet speed regulation via FLC as illus-
trated in Figure 7. The FLC (Collotta et al., 2014)utilizes
temperature forecasts from the ANN, represented as Ti,
along with the difference between Tiand its value from
the previous time step, Ti. This information is used to
dynamically adjust its membership functions to better
accommodate the subjective aspects of thermal comfort,
which can vary even within the same individual. Experi-
mental results are demonstrated on an embedded proto-
typing board, showcasing the system’s ability to manage
fan coil-induced air speed, predict indoor temperatures,
and adapt membership functions based on user feed-
back. A modified approach is introduced in Marvuglia
et al. (2014) where the indoor temperatures, serving as
inputs to the FLC, are predicted using an auto-regressive
neural network with external inputs (NNARX). This model
is developed based on variables such as outdoor temper-
ature, air relative humidity, and wind speed. In another
work by Kang et al. (2015), a hybrid control system that
combines simple on-off control with FL is proposed to
address delays in building thermal responses, aiming to
reduce energy waste and improve comfort. In the pro-
posed controller, the operating time is calculated using
the difference between the set point and the current
room air temperature, denoted as eT, and the rate of tem-
perature change, dT/dt.This approach leverages the sim-
plicity of on-off control while enhancing its adaptability
and performance through the application of FL.
To simultaneously control both the indoor tempera-
ture and humidity, a novel Proportional-Derivative (PD)-
based FLC has been developed in Z. Li et al. (2015a)for
a variable speed (VS) direct expansion (DX) AC system,
featuring two control loops that manage indoor dry-bulb
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 13
Figure 7. Illustration of the HVAC system architecture proposed in Collotta et al. (2014).
temperature (Tdb) and wet-bulb temperature (Twb ) using
varying fan and compressor speeds, respectively. Subse-
quently, an enhancement was introduced in Z. Li et al.
(2015b) that integrates FLC with an ANN to separate
the temperature and humidity control loops, employ-
ing sensible and latent cooling capacities as intermedi-
ary variables. Additionally, the FL system was refined to
simplify its computations and architecture by adjusting
the weights assigned to linguistic variables. A modified
approach presented in H. Yan et al. (2018) utilizes the error
between the dry-bulb temperature and its desired tem-
perature etdb, as well as the error between the wet-bulb
temperature and its desired temperature, ewdb,along
with rate of change of these temperatures, denoted as
dTdb/dt and dTwb /dt as input variables of the FLC. The
study in A.-H. Attia et al. (2015) developed a theoretical
model of a fan-coil unit and heat transfer between air
and coolant fluid, focussing on controlling room tempera-
ture and relative humidity. The control actions adjust the
percentages of chilled and hot water flow rates in sum-
mer, and hot water and steam flow rates in winter. The
findings indicate that the FLC maintains the desired com-
fort zone with lower energy consumption compared to a
traditional PID controller.
4.2.2. Greenhouse (GH)
Another application of FLCs that has garnered signifi-
cant attention in the literature in recent years is the GH
conditioning system (Mohamed & Hameed, 2018;Naj-
murrokhman et al., 2019;Ngetal.,2023; Revathi & Sivaku-
maran, 2016). In Ali et al. (2016)andRiahietal.(2020),
both eTand eHare used to control four parameters, i.e.
ventilation rate (VR), heating rate (HR), humidification
rate (HuR) and dehumidification rate (DHuR). The work
in Robles Algarin et al. (2017) proposed a low-cost FLC by
using an Arduino board to measure eTand eH.Thissys-
tem controls temperature, soil moisture, relative humid-
ity, and lighting within a GH prototype by regulating the
power of extractors, heaters, and humidifiers. Additional
input variables for FLCs were introduced in Azaza et al.
(2016), including eCO2and illumination error (eiL)toman-
age ambient lighting and CO2in a smart GH via artificial
lighting (AL) and CO2level.
In L. Wang and Zhang (2018), a hierarchical control
approach to regulate temperature in solar GHs is intro-
duced with the aim to optimize diurnal and nocturnal
temperatures (Tdand Tn) for enhanced crop growth,
specifically using tomatoes as a case study. The study
proposes an adaptive FLC method that adjusts the
rolling curtain based on climatic variables to maintain
ideal greenhouse temperatures, especially during freez-
ing conditions, ensuring that temperatures are closely
tracked day and night for optimal crop development.
Four input variables are used, i.e. Td,Tn,andrelative
humidity values inside and outside the GH (Hiand Ho).
In another work by Alpay and Erdem (Alpay & Erdem,
2018), the FLC system includes four inputs and five out-
puts. The inputs are Ta,Ha, soil moisture (SM), and light
intensity (Lux). The outputs are heating, cooling, irriga-
tion, lighting, and shading. In the system, sensor nodes
equipped with Zigbee wireless technology monitored the
GH’s indoor climate. This setup allowed for adding more
14 H. H. TANG AND N. S. AHMAD
sensor nodes, spreading them over a larger area, and
ensuring that other sensors continued to operate even
if one failed. The study by Vanegas-Ayala et al. (2023)
enhances humidity prediction in a GH using a Mamdani-
type fuzzy inference system optimized through a hybrid
method combining GA and interior point algorithm (IPA).
The research highlights that air temperature (Ta), CO2,
and SM significantly influence GH humidity. The pro-
posed method achieved a prediction accuracy for rela-
tive humidity with an effectiveness of 90.97% and high
precision.
4.2.3. Summary
Building on previous discussions, Table 3provides a sum-
mary of FL applications aimed at enhancing ambient
conditioning systems, specifically in HVAC and GH envi-
ronments, as documented in the literature from 2014 to
2023. The summary shows that FL has been widely used
to enhance the performance of HVAC and GH systems by
improving energy consumption, thermal comfort, control
accuracy, and system reliability. Different optimization
techniques, such as heuristic methods, ANN, and GA, have
been employed to further enhance the performance of
the FLCs.
4.3. Robotics and autonomous systems (RAS)
RAS represent a diverse collection of intelligent machines
engineered to execute tasks autonomously. FL is partic-
ularly influential in the realm of navigation and motion
control within RAS, where precision and adaptability are
paramount (Teo et al., 2022). In scenarios where tradi-
tional controllers may fail due to the high degree of
system complexity or unpredictable environmental fac-
tors, FL offers a robust alternative (Rajendran et al., 2019).
By employing fuzzy sets and rules that mimic human
decision-making, it allows for more nuanced control
strategies that can dynamically adjust to varying condi-
tions. This is especially beneficial in applications such as
autonomous vehicles and robotic arms in manufacturing
where motion control must be both precise and flexible
(Ahmad et al., 2022; Loganathan & Ahmad, 2023). These
systems can thus handle tasks such as navigation and
object handling more efficiently, significantly enhancing
operational capabilities and safety in dynamic or unstruc-
tured environments (Arrouch, Ahmad, et al., 2022). The
following sections delve into the applications of FL in
motion control and autonomous navigation, highlighting
significant advancements documented in the literature
over the past decade.
4.3.1. Motion control
The effective control of robot motion and speed relies
on managing the dynamic interactions between the elec-
trical and mechanical components of robotic systems.
However, these systems often face uncertainties and non-
linearities in motors and actuators that drive the robots
(Breesam et al., 2022; Syed Mubarak Ali et al., 2019).
For example, saturation occurs when a motor’s output
ceases to increase despite higher input signals beyond
a specific threshold (Ahmad et al., 2013;Chanetal.,
2017). Another common issue is backlash, where slack
in mechanical components causes response delays or
inconsistencies. These nonlinearities directly affect the
precision of motion and speed control, making it essen-
tial to incorporate flexible, adaptive control solutions like
FL. Additionally, uncertainties arise due to variable time-
delays (D. Sun et al., 2018), load conditions, external dis-
turbances, or wear and tear that affects motor perfor-
mance unpredictably (Ahmad, 2017; Ting et al., 2024).
Unlike the previously discussed EH and ACS, where FLC
often features similar inputs and outputs, FL has been uti-
lized in diverse ways to treat systems that are subject to
perturbations as depicted in Figure 8. In Figure 8(a), FL
functions as the primary compensator which can have
structures similar to other controllers such as SMC and
PID (Garcia-Martinez et al., 2020). In Figure 8(b), FL is used
to parameterize the main controller (Elawady et al., 2020;
H.-m.Lietal.,2016), while in Figure 8(c), it is used to
compensate for the perturbations in the system (Ahmad,
2020).
In applications demanding precise movements, like
surgical robots or intricate manipulation tasks, minimiz-
ing the effects of deadzones is critical. Deadzones are
regions where varying the input signal does not pro-
duce a response or output. Compensating for dead-
zones is challenging due to their unknown, asym-
metric nature, which is influenced by the mechani-
cal properties of the motors and the characteristics of
the power supply (Ahmad, 2020). Several studies have
employed FL to mitigate deadzone nonlinearities in
both single-link and multi-link flexible arm manipula-
tors as reported in Jang (2019), Z. Zhao et al. (2023),
Y. Jiang et al. (2015), Zhang (2022), D. Vu et al. (2019),
and Huang and Huang (2023). These studies ensure
the stability of closed-loop systems through the Lya-
punov function approach. In Chen et al. (2019), an
adaptive deadzone fuzzy compensator is designed to
mitigate the effect of deadzone nonlinearity on the
actuators of underwater vehicles. By adapting to the
unknown characteristics of the input deadzone, the com-
pensator has demonstrated improved trajectory tracking
control.
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 15
Tab le 3 . Summary of FL applications to improve ambient conditioning systems as reported in the literature from 2014 to 2023.
Ref Year App. FIS FL inputs FL outputs Modification Advantages / Role of FL
Shah et al. (2020) 2020 HVAC M eT,eHFS, chilled-water flow,
power
Heuristic smoothes energy consumption of an air conditioning load
when subject to thermal disturbances
Abdo et al. (2018) 2018 HVAC M eT,eT,eP,eP,eCO2,eCO2FS, hot-water flow, fresh air
dampers
Heuristic minimal overshoot, errors, and faster response time
Mohamed and Mohamed
(2015)
2015 HVAC M Ta,eT,Tdew,VeFS, CS, operation mode, fin
direction
Heuristic allows multiple control variables
Francis et al. (2022) 2022 HVAC M Ta,eT,Tdew, NoC, ToD, weather conditions CS, operation mode Heuristic reduces energy consumption, improves thermal comfort
Chojecki et al. (2023) 2023 HVAC M eT,eTHeater/cooler power con-
trol
PI-FLC reduces energy consumption, improves thermal comfort
Rajeswari Subramaniam
et al. (2023)
2023 HVAC M PMV, CO2,TaFS, recirculation flaps,
coolant flow rate
Heuristic regulates in-cabin PMV level and CO2 concentration at
desirable levels
Collotta et al. (2014) 2014 HVAC M Ta,TaInlet air speed ANN-FLC improves FLC membership function adjustment based on
user feedback
Marvuglia et al. (2014) 2014 HVAC M Ta,TaInlet air speed NNARX-FLC improves temperature forecasting performance
Kang et al. (2015) 2015 HVAC M eT,dT/dt Operating time On/off-FLC minimizes delays in thermal responses, reduces energy
waste and improves comfort
Z. Li et al. (2015a) 2015 HVAC M Ta,Tdb ,Twb CS, FS PD-FLC simultaneous control of indoor temperature and humidity
for VS DX AC system
Z. Li et al. (2015b) 2015 HVAC M Ta,Tdb,Twb CS, FS ANN-FLC simultaneous control of indoor temperature and humidity
for VS DX AC system
H. Yan et al. (2018) 2018 HVAC M etdb ,etwb,dTdb /dt,dTdb/dt CS, FS Heuristic Simultaneous control of indoor temperature and humidity
for VS DX AC system
A.-H. Attia et al. (2015) 2015 HVAC M eT,eHChilled- and hot-water
flows
Heuristic maintains the desired comfort zone with lower energy con-
sumption compared to a traditional PID controller
Abuhussain et al. (2023) 2023 HVAC M eT,eH,NoC,Eprice Operation mode Heuristic reduces energy consumption, improves thermal comfort
Najmurrokhman et al.
(2019)
2019 GH M eT,eHFS Heuristic Temperature and humidity control system in oyster mush-
room cultivation
Revathi and Sivakumaran
(2016)
2016 GH M eT,eTHeating power PSO maintains temperature between 15◦C and 20◦Cwithout
exact mathematical model of GH
Ali et al. (2016) 2016 GH M eT,eHVR, HR, HuR, DHuR Heuristic increases indoor temperature overnight and decreased
indoor temperature during the day
Riahi et al. (2020) 2020 GH M eT,eHVR, HR, HuR, DHuR Heuristic increases indoor temperature overnight and decreased
indoor temperature during the day
Robles Algarin et al. (2017) 2017 GH M eT,eHpower of extractors,
heaters, and humidifiers
Heuristic optimal use of resources for a gable roof GH prototype
Azaza et al. (2016) 2016 GH M eT,eH,eCO2,eiL VR, HR, HuR, DHuR, AL, CO2
level
Heuristic reduces power and water consumption
L. Wang and Zhang (2018) 2018 GH T-S Td,Tn,Hi,HoRolling curtain control Heuristic improves crop growth during freezing conditions
Alpay and Erdem (2018) 2018 GH M Ta,H, SM, Lux Heating, cooling, irrigation,
lighting, and shading
Heuristic improves scalability with wireless sensor network
Vanegas-Ayala et al. (2023) 2023 GH M Ta,CO
2, SM Luminosity and light inten-
sity, irrigation, CO2level
GA, IPA improves prediction accuracy for relative humidity of GH
16 H. H. TANG AND N. S. AHMAD
Figure 8. Examples demonstrating the various ways in which FL can be utilized to control perturbed systems. (a) FLC as the main
compensator. (b) FL is used to parameterize the main controller and (c) FL is used to compensate for the perturbations.
Saturation is a type of nonlinearity that arises because
actuators, such as motors or hydraulic systems, can only
exert a certain maximum force or torque. When the
control input exceeds this limit, the actuator saturates,
introducing a nonlinear relationship between the con-
trol input and the resulting motion or force (Chotikun-
nan & Pititheeraphab, 2023). The study in Chang et al.
(2021) utilizes a FL system along with a smooth func-
tion to approximate unknown nonlinearities and actuator
saturation in a single-link robotic manipulator system.
This approach aims to ensure that all variables within the
closed-loop system remain bounded, allowing the system
output to closely track the given reference signal. In Zhu
et al. (2022), the challenge of actuator saturation in trajec-
tory tracking control for unmanned underwater vehicles
(UUVs) is addressed through a FL-based cascade con-
trol strategy. This strategy effectively mitigates excessive
speed references and confines dynamic outputs (forces)
within acceptable domains. A similar methodology is
proposed in Tilahun et al. (2023) for a differential drive
wheeled mobile robot (DWMR), addressing actuator sat-
uration and dynamic disturbances. Yanchao Sun et al.
(2023) introduce an adaptive interval type-2 fuzzy NN to
enhance trajectory tracking for a multi-legged underwa-
ter robot facing input saturation and full-state constraints.
Additionally, in Urrea et al.’s work (Urrea et al., 2020),
FLC demonstrates superior performance compared to PID
control in trajectory tracking for the joints of a 2-DoF
planar manipulator robot.
Uncertainties in robotic systems may also arise due
mechanical friction or disturbances caused by varying
loads (Fan et al., 2020). The research detailed in Chao et al.
(2019) introduced a GA-optimized fuzzy PID controller,
designed specifically for DC motor speed control under
load disturbances. In the work, the conventional PID con-
troller design was used to form an equivalent FLC with
four parameters defining the operating ranges of the
input and output variables. In a similar vein, the study
in Premkumar and Manikandan (2015) explored a fuzzy
PD controller that was optimized using a bat algorithm
(BA). This method not only outperformed the same con-
troller optimized with other SI but also exceeded the
capabilities of other optimized fuzzy PID controllers in
controlling the speed of a BLDC motor. To cope with para-
metric and nonparametric uncertainties in the robot’s
dynamic model, a Mamdani-type FL controller optimized
using PSO is introduced in Abadi and Khooban (2015).
Obadina et al. (2022) focussed on trajectory control of a
leader-follower manipulator system where a fuzzy PD+I
compensator is designed to enhance tracking of uncer-
tain trajectories. In Ghafarian et al. (2020), an adaptive
fuzzy SMC is proposed to compensate for uncertain-
ties and nonlinearities including the hysteresis effect and
external disturbances in the motion of a 3-degrees-of-
freedom (3-DoF) XYZ micro/nano manipulator. The study
in Leal et al. (2021) demonstrated that a GA-optimized
Fuzzy-PD controller with a T-S type outperforms GA-
optimized PID and several other controllers in motion
control of an unmanned aerial vehicle (UAV) subject to
external disturbances.
A different approach to improve the speed control of
a DC motor subject to parameter variations and exter-
nal disturbance is introduced in Elawady et al. (2020)
where the FL and GA are hybridized to form an opti-
mization algorithm to parameterize an adaptive SMC-PID
controller. The study in Tuan et al. (2018) presents an
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 17
adaptive fuzzy SMC for joint position tracking control of a
robotic manipulator which is represented as an uncertain
nonlinear second-order systems. The proposed controller
leverages a non-singular fast terminal sliding variable and
a continuous control algorithm to achieve rapid conver-
gence and strong robustness, while using an adaptive FLC
algorithm to approximate the switching control law and
address the limitations of identifying upper bounds for
perturbations and uncertainties. In another work (Truong
et al., 2019), an adaptive fuzzy position control strategy
is proposed for a 3-DOF hydraulic manipulator with sig-
nificant payload variations, combining backstepping SMC
(BST-SMC), FL, and a nonlinear disturbance observer. The
strategy uses BST-SMC for manipulator dynamics and
PI control for actuator dynamics, while the FL system
dynamically adjusts the control and robust gains based
on nonlinear disturbance observer output to effectively
compensate for varying payloads.
FL has also been leveraged to compensate for uncer-
tainties through integration with time-delay control
(TDC), a method that has recently gained significant
attention in the literature (Wu et al., 2015). TDC, which is
based on the time-delay estimation (TDE) technique, is a
model-free control approach that utilizes data from the
previous time period to estimate and address unknown
system dynamics. It assumes minimal system dynamic
changes within a sampling period, allowing for the devel-
opment of a simple, robust, and efficient control method
without prior knowledge or offline identification. This
approach has found broad application in controlling
robotic manipulators and chaotic systems (Kali et al.,
2018). In Kim et al. (2017) for instance, a fuzzy TDC was
proposed using TDE and desired fuzzy error dynamics,
with a conversion formula derived to translate the fuzzy
TDC to a fuzzy PID controller in the discrete-time domain.
The proposed controller in Bae et al. (2017) integrates
TDE to estimate and counteract continuous nonlineari-
ties in robot dynamics, while leveraging FL systems to
mitigate TDE errors caused by discontinuous nonlinear-
ities like friction. The study in Y. Sun, Liang, et al. (2023)
proposed a novel friction compensation controller that
combinesTDEwithanadaptiveFLsystem(AFLS).TheTDE
component estimates unknown system dynamics using
prior sampling data, while the AFLS compensates for hard
nonlinearities and minimizes errors caused by TDE, ulti-
mately improving the robotic arm’s tracking accuracy. To
address the nonlinear, saturated hysteretic behaviour of
shape memory alloy (SMA) actuators, integration of FL
and TDC is able to minimize position errors and over-
shoot compared to TDC and PI controllers as documented
in J. Li and Pi (2021). Van et al. (2020) proposed a novel
robust controller that integrates a self-tuning fuzzy PID,
SMC and TDC to offer faster transient response, finite-time
convergence, reduced steady-state error, and chattering
elimination for a robot manipulator that is subject to
unknown dynamics.
Another area where the hybrid application of FL and
TDC has garnered increasing interest is in addressing the
challenges of structural parametric uncertainty and the
complexity of cable transmission models in cable-driven
robots or manipulators (M.-T. Vu et al., 2022). The study
in S. Jiang et al. (2019) for instance demonstrated a tech-
nique that uses TDC to estimate unknown dynamics and
disturbances, alongside a fuzzy PD controller for self-
tuning parameters which effectively reduces errors due to
system uncertainties and external disturbances. In F. Yan
et al. (2021), an adaptive TDE technique combined with
SMC as illustrated in Figure 9is proposed which utilizes an
adaptive approach with a FL algorithm to enhance input-
output mapping and control gain flexibility. This method
compensates for unmodeled dynamics and disturbances,
and using the desired trajectory as an input, the controller
improves performance and maintains uniform bounded-
ness.
4.3.2. Autonomous navigation
In the realm of autonomous vehicles (AVs), FL has been
utilized to design lateral control law for determining refer-
ence driving speed (X. Wang et al., 2015), and to enhance
handling of the unpredictable conditions encountered
in real-world driving scenarios (Basjaruddin et al., 2014;
Lin & Nguyen, 2019; Mahmood et al., 2023). One critical
application of AVs is Adaptive Cruise Control (ACC), part
of Advanced Driver Assistance Systems (ADAS), which
automatically adjusts vehicle speed to maintain a safe dis-
tance from the vehicle ahead. ACC functions like standard
cruise control when the road is clear, maintaining a pre-
set speed. However, if it detects a slower vehicle within
a designated range, it reduces speed to ensure safe and
accurate following at or below the set speed.
For lateral motion, ACC with FLC which takes the dis-
tance and speed errors as inputs, and acceleration com-
mand as output, demonstrated better performance com-
pared to ACC with PID control as reported in Alomari et al.
(2020). In the case of double-lane-change maneuvers at
different forward speeds and subjected to structured and
unmodeled uncertainties, the study by Mohammadzadeh
and Taghavifar (2020) introduced a robust FLC utilizing
a novel non-singleton fuzzy system with non-stationary
fuzzy sets for real-time dynamics estimation. Mao et al.
(2021) proposed a fusion of FL and model predictive con-
trol (MPC) for ACC in uncertain traffic environments, as
illustrated in Figure 10. In their study, a variable weight
coefficient based on fuzzy control theory is introduced
to improve the adaptability of the ACC system, ensuring
smooth and safe following of the target vehicle. A slightly
18 H. H. TANG AND N. S. AHMAD
Figure 9. Illustration of an adaptive TDC for cable-driven manipulators using FL for parameter auto-tuning (F. Yan et al., 2021).
Figure 10. Illustration of an ACC system co-simulation environment established in Mao et al. (2021).
different approach is introduced in Guo et al. (2023)
where a fuzzy-MPC is adopted to establish performance
indicators, including tracking performance and comfort,
as objective functions. This approach dynamically adjusts
their weights and determines constraint conditions based
on safety indicators to adapt to continuously changing
driving scenarios.
ACC in more challenging environments, such as round-
abouts, has also been reported in the literature. For exam-
ple, Rastelli and Peñas (2015) require inputs of angular
error, lateral error, distance to bend, and actual speed
to the FLC to control the steering position and angu-
lar speed of the vehicle. In another study, M. A. H. Ali
et al. (2020) applied FL to the high-level control of the
system to detect the presence of a roundabout by identi-
fying the right curb, left curb, and elliptical curve through
image processing. For shared steering control of semi-
autonomous vehicles with uncertainty from imprecise
parameters due to time-varying vehicle speed and driver
state, the study in Y. Liu et al. (2022) proposed a type-
2 FLC with stability guaranteed via the direct Lyapunov
method.
Another application of FL in the realm of autonomous
navigation is mobile robot path planning which includes
collision avoidance. In Sabry (2018), the FL is used to
control longitudinal and lateral directions of a wheeled
mobile robot (WMR) in the presence of obstacles. The
study in Mondal et al. (2022) introduced a hybrid con-
trol law that combines a FLC with a feedback linearization
controller (FBC) as depicted in Figure 11. The FBC helps
the mobile robot remain on its desired path, while the
FLC assists in avoiding critical obstacles around the ref-
erence path. It is analytically proven that the proposed
hybrid control law asymptotically converges the robot
to the desired path when it is safely away from obsta-
cles. Furthermore, when the robot detects an obstacle
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 19
Figure 11. Block diagram of the proposed hybrid FLC-FBC in Mondal et al. (2022) for path following of a DWMR.
within its sensing range, the obstacle avoidance control
law acts locally, causing the robot to deviate from the
desired path to avoid collision, while maintaining sta-
bility through a bounded tracking control law. Consid-
ering bounded sensor measurement uncertainties dur-
ing obstacle detection, the FL-based collision-avoidance
algorithm demonstrates robust behaviour.
To further enhance the FL-based path planning strat-
egy of mobile robots, several techniques have been pro-
posed, including employing SI to optimize membership
functions and hybridization with other methods (Ahmad,
2023; Loganathan & Ahmad, 2024;Najmetal.,2024; Yilin
& Jianhua, 2022). For instance, in Kuo et al. (2017), PSO
is used, while in R. Zhao et al. (2015), GA is applied to
improve the FL membership functions. Štefek et al. (2021)
utilized GA to tune the membership function in order
to minimize the energy consumption of a DWMR while
maintaining its motion control performance. Another
tuning approach based on a wind-driven mechanism is
proposed in A. Pandey and Parhi (2017) to optimize and
set the antecedent and consequent parameters of the FL
control, applicable in both unknown static and dynamic
environments.
In contrast, the work in Kamil and Moghrabiah (2022)
introduced a multilayer decision-based FL model, which
demonstrated improved performance in terms of run-
time and path length compared to other existing solu-
tions such as the rapidly-exploring random tree (RRT),
dynamic window approach (DWA), and bug algorithm
when tested in environments with static and dynamic
obstacles. Y. Sun, Wang, et al. (2023) employed FL to
optimize the DWA’s weights, resulting in improved run-
time and path length for local robot path planning.
A similar approach is introduced in Akka and Khaber
(2018), where FL is used to dynamically adjust the
weights of the terms included in the DWA’s objective
function according to different environmental scenar-
ios. In Bajrami et al. (2015), FL is hybridized with NN,
with obstacle positions relative to the robot serving as
inputs to the FL and commands to the robot’s wheels
serving as outputs. The study in Hu et al. (2020)pro-
posed a fuzzy-observer-based composite nonlinear feed-
back (CNF) controller using a T-S vehicle lateral dynamic
model to ensure normal path-tracking and improve tran-
sient performance in the face of actuator saturation and
disturbances.
4.3.3. Summary
Table 4summarizes how FL is employed and modified
to address various types of perturbations in RAS as dis-
cussed in the preceding subsections. The studies cover a
broad range of applications, including robotic manipula-
tors (RM), WMR, DWMR, UUV, UAV, teleoperated systems,
and ACC, among others. The table highlights the diver-
sity of perturbations addressed by FL, such as deadzone
compensation, actuator saturation, unknown dynamics,
parameter variations, internal disturbances like backlash
and friction, external disturbances, load disturbances,
and environmental changes. Each study is noted for the
type of stability analysis conducted, with Lyapunov meth-
ods frequently employed to ensure robust control per-
formance. Key advantages of FL applications are noted
for each study, such as enhancing trajectory tracking,
improving control precision, minimizing errors, and com-
pensating for nonlinearities and uncertainties. This exten-
sive review underscores the versatility and effectiveness
of FL in managing a wide array of challenges in RAS, con-
tributing to the advancement of autonomous and robotic
technologies.
5. Analysis and potential research directions
Building upon previous discussions, this section provides
a comparative analysis of FL applications for address-
ing perturbations in EH, ACS, and RAS domains. It sum-
marizes the advantages and disadvantages of the FL
approach in these areas based on the reviewed literature,
highlights current trends, identifies gaps, and suggests
potential research directions to further advance these
fields.
20 H. H. TANG AND N. S. AHMAD
Tab le 4 . Summary of FL applications to improve RAS subject to various types of perturbations, as reported in the literature from 2014 to 2023.
Ref Year Source of perturbations Modification/Hybrid Application FIS Stability Analysis Advantages/Role of FL
Z. Zhao et al. (2023) 2023 Uncertain
deadzone
Adaptive Single -link RM T-S Lyapunov Deadzone compensation
Jang (2019) 2019 Uncertain
deadzone
Adaptive Single -link RM T-S Lyapunov Deadzone compensation
Y. Jiang et al. (2015) 2015 Uncertain
deadzone
Adaptive Multi-link RM M Lyapunov To approximate the manipulator’s dynamics
Zhang (2022) 2022 Uncertain
deadzone
Adaptive Multi-link RM T-S Lyapunov Deadzone compensation
D. Vu et al. (2019) 2019 Uncertain
deadzone
FL-NN Multi-link RM T-S Lyapunov Deadzone compensation
Huang and Huang (2023) 2023 Uncertain
deadzone
Adaptive Multi-link RM T-S Lyapunov Approximates uncertainties and deadzones
Chen et al. (2019) 2019 Uncertain
deadzone
Adaptive UUV T-S Lyapunov Deadzone compensation
Ahmad (2020) 2020 Uncertain
deadzone
H∞-PI WMR T-S H∞bound Approximates deadzone functions
Chotikunnan and Pititheeraphab (2023) 2023 Actuator saturation Adaptive P Multi-link RM M x Reduces overshoot and settling time when subject
to actuator saturation
Zhu et al. (2022) 2022 Actuator saturation Fuzzy-SMC UUV T-S Lyapunov Compensates for actuator saturation and dynamic
disturbances
Breesam et al. (2022) 2022 Actuator
saturation, load
FL-NN Multi-motor M x Improves speed response and motor synchroniza-
tion compared to PI control
Chang et al. (2021) 2021 Unknown non-
linearity, actuator
saturation
FL-BST Single-link RM T-S Lyapunov Approximates unknown nonlinearity and satura-
tion
Tilahun et al. (2023) 2023 Saturation,
dynamic
disturbance
SMC-ANFIS DWMR M Lyapunov Improves robustness against parameter variations
and external disturbance
Yanchao Sun et al. (2023) 2023 State satura-
tion, unknown
disturbance
FL-NN Multi-legged UUV T-S Lyapunov Improves trajectory tracking in the presence of
input saturation and full-state constraints
D. Sun et al. (2018) 2018 Time-varying
delays
Adaptive Teleoperated systems T-S Lyapunov Estimates the external torques and improve the
operator’s force perception for the environment
Urrea et al. (2020) 2020 Frictions FL-PD Multi-link RM M x Improves trajectory tracking in the presence of fric-
tions
Fan et al. (2020) 2020 Unknown
dynamics
Adaptive Multi-link RM T-S Small gain theorem Minimizes on-line learning computation burden in
conventional FL systems
Chao et al. (2019) 2019 Load disturbance FL-PID, GA DC motor T-S x Improves speed control under load disturbances
Premkumar and Manikandan (2015) 2015 Load disturbance FL-PID, BAT BLDC motor T-S x Improves speed control under load disturbances
Abadi and Khooban (2015) 2015 Parameter
variations
FL-PD,PSO DWMR M x Improves trajectory tracking in the presence of
parameter variations
Obadina et al. (2022) 2022 Uncertain trajecto-
ries
FL-PD+I Multi-link RM M x Enhances tracking of uncertain trajectories
Ghafarian et al. (2020) 2020 Backlash, wear, fric-
tions, hysteresis
FL-SMC Multi-link RM T-S Lyapunov Improves precision motion tracking and tracking
errors compared to PID control
Ref Year Source of perturba-
tions
Modification/Hybrid Application FIS Stability Analysis Advantages/Role of FL
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 21
Leal et al. (2021) 2021 External
disturbances
FL-PD,GA UAV T-S x Addresses limitations of PID control in handling
nonlinearity
Garcia-Martinez et al. (2020) 2020 Load disturbance FL-PID, GA DC motor M x Reduces rise time and settling time for DC motor
position control
Elawadyetal.(2020) 2020 Parameter vari-
ations, external
disturbances
SMC-PID, GA DC motor M Lyapunov Improves tracking control performance over PID
and SMC
Tuan et al. (2018) 2018 Parameter
variations
Fuzzy-SMC Multi-link RM T-S Lyapunov Approximates switching control law in SMC
Truong et al. (2019) 2019 Load disturbance BST-SMC Multi-link RM M Lyapunov Compensates for large varying payloads
Kim et al. (2017) 2017 Load disturbance FL-TDC Multi-link RM T-S x Improves robustness against load disturbance
Bae et al. (2017) 2017 Nonlinearities in
robot dynamics
FL-TDC Multi-link RM M Lyapunov Mitigates TDE errors caused by discontinuous non-
linearities like friction
Y. Sun,Liang, et al. (2023) 2023 Frictions FL-TDC Multi-link RM M Lyapunov Compensates for hard nonlinearities and minimizes
errors caused by TDE
J. Li and Pi (2021) 2021 Saturated
hysteretic SMA
FL-TDC SMA actuator M x Addresses the nonlinear, saturated hysteretic
behaviour of SMA
Van et al. (2020) 2020 Unknown
dynamics
FL-PID-SMC-TDC Multi-link RM M Lyapunov Improves transient response, reduce steady-state
error, and eliminate chattering
M.-T. Vu et al. (2022) 2022 Cable ten-
sile strength,
unmodeled
dynamics
Adaptive FL – TDC Cable-driven robots M Lyapunov Addresses complexity, lack of accurate modelling,
and extreme performance specifications
S. Jiang et al. (2019) 2019 Unmodeled
dynamics, external
disturbances
Fuzzy PD – TDC Cable-driven robots M x Tunes PD controller to reduce errors due system’s
uncertainties and external disturbances
F. Yan et a l. (2021) 2021 Unmodeled
dynamics,
disturbances
SMC-TDC Cable-driven robots M Lyapunov Enhances input-output mapping and control gain
flexibility
X. Wang et al. (2015) 2015 Reference path
data noise
Heuristic AV lateral control M Lyapunov Improves the steering controller for lateral motion
Basjaruddin et al. (2014) 2014 Sensor mea-
surement
uncertainties
Heuristic ACC-speed control M x Enhances ACC using distance and change of dis-
tance from the preceding vehicle
Lin and Nguyen (2019) 2019 Controller and
speed imitations
Fuzzy-NN ACC-speed control T-S Lyapunov Improves safety, riding comfort and fuel efficiency
in cooperative ACC system
Mahmood et al. (2023) 2023 Uncertain driving
conditions
Heuristic ACC-break, throttle control M x Controls break and throttle based on the distance
from and speed of the preceding vehicle
Ref Year Source of perturba-
tions
Modification/Hybrid Application FIS Stability Analysis Advantages/Role of FL
Alomari et al. (2020) 2020 Uncertain driving
conditions
Heuristic ACC-acceleration control M x Improves lateral motion based on errors in distance
and speed
Mao et al. (2021) 2021 Uncertain driving
conditions, speed
limit
Fuzzy-MPC Multi-objective ACC M x Improves safety, riding comfort and fuel efficienc y
in cooperative ACC system
Guo et al. (2023) 2023 Uncertain driving
conditions
Fuzzy-MPC Multi-objective ACC M x Tunes the weight factor based on the vehicle’s driv-
ing state.
(continued).
22 H. H. TANG AND N. S. AHMAD
Tab le 4 . Continued.
Ref Year Source of perturbations Modification/Hybrid Application FIS Stability Analysis Advantages/Role of FL
Rastelli and Peñas (2015) 2015 Lateral and angu-
lar error measure-
ments
Heuristic ACC on roundabouts M x Controlsthe steering position and angular speed on
roundabouts
M. A. H. Ali et al. (2020) 2020 Roundabout
images
Heuristic ACC on roundabouts M x Detects the presence of a roundabout by identify-
ing the right curb, left curb, and elliptical curve
Y. Liu e t al. ( 2022) 2022 Time-varying vehi-
cle speed, lateral
wind force
Heuristic ACC-shared driving T-S Lyapunov Improves driver-automation shared lane keeping
control of semi-sutonomous vehicle
Sabry (2018) 2018 Wheel’s nonlinear-
ity
Heuristic WMR obstacle avoidance M x Improves the vehicle’s longitudinal and lateral
directions
Mondal et al. (2022) 2022 Sensor mea-
surement
uncertainties
FLC-FBC DWMR path following T-S Error bound Improves robustness against sensor measurement
uncertainties during obstacle detection
Kuo et al. (2017) 2017 Sensor mea-
surement
uncertainties
PSO Robot path planning M x Improves robot path planning in the presence of
obstacles
R. Zhao et al. (2015) 2015 Sensor mea-
surement
uncertainties
GA DWMR path planning M x Improves robot path planning in the presence of
obstacles, and reduce runtime
Štefek et al. (2021) 2021 Unknown environ-
ments
GA DWMR path planning M x Reduces energy consumption while maintaining
motion control performance
A. Pandey and Parhi (2017) 2017 Unknown environ-
ments
WDO Robot path planning M x Improves robot path planning in the presence of
static and dynamic obstacles
Kamil and Moghrabiah (2022) 2022 Unknown environ-
ments
Multilayer FL DWMR path planning M x Improves path length and runtime compared to
RRT, DWA, and bug algorithm
Y. Sun,Wang, et al. (2023) 2023 Unknown environ-
ments
Fuzzy-DWA DWMR path planning M x Optimizes the DWA weights, resulting in improved
runtime and path length
Akka and Khaber (2018) 2018 Unknown environ-
ments
Fuzzy-DWA DWMR path planning M x Dynamically adjusts the DWA’s weights according
to different environmental scenarios
Bajrami et al. (2015) 2015 Unknown environ-
ments
Fuzzy-NN DWMR path planning M x Provides commands to the robot wheels based on
sensor readings
Hu et al. (2020) 2022 Actuator sat-
urations,
disturbances
Observer CNF AV path tracking T-S Lyapunov Improves path tracking control in the face of actua-
tor saturation and disturbances.
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 23
Tab le 5 . Summary of main advantages and challenges of FL approach in EH, ACS, and RAS domains.
Domain Advantages of FL Approach Challenges in FL Approach
EH •Enhanced energy production efficiency, reduced operational
costs, improved reliability.
•Balancing computational load with rapid response needs.
•Integratable with control methods like P&O and SMC. •Integrating multiple components and subsystems into a
cohesive FLC framework for MPPT requires comprehensive sys-
tem understanding and modelling.
•Improves transient response and tracking accuracy. •Optimizing FL rules and membership functions via SI needs
expertize in initialization strategies and parameter constraints.
•Reduces control system chattering.
ACS •Systematic control of variables like inlet air speed, operating
time,waterflows,heatingpower.
•Complex design for large-scale systems.
•Minimizes thermal response delays, enhances thermal com-
fort, and reduces power and water consumption.
•Handles uncertainties and inaccuracies in sensor measure-
ments and environmental predictions.
•Maintains comfort with lower energy consumption than tra-
ditional PID controllers.
•Needs adaptability to rapid changes in HVAC and greenhouse
settings without frequent recalibration.
RAS •Compensates for deadzone and actuator saturation. •May not achieve precise control for high-speed dynamics.
•Enhances overall system performance by dynamically fine-
tuning controller parameters.
•Rule base complexity increases with system complexity.
•Facilitates real-time decision-making. •Potential instability in closed-loop systems.
•Integrates well with other controlmethods like PID,TDC, SMC,
MPC, and FBC.
•Difficulties in precise mathematical modelling complicates
deriving stability conditions.
•Suitable for systems with imprecise sensor data. •Potential overfitting when optimizing FL membership func-
tions based on specific scenarios.
5.1. Comparative analysis
Table 5summarizes the main advantages and challenges
of the FL approach in EH, ACS, and RAS based on the liter-
ature review. In the EH domain, FL enhances energy effi-
ciency and system stability, although complexity and sen-
sitivity to tuning present significant challenges. For ACS,
benefits include improved energy consumption and ther-
mal comfort, but design complexity and the need for reg-
ular updates are notable drawbacks. In the RAS domain,
FL provides robust control and adaptability; however, it
may face difficulties with precise control in high-speed
dynamics and can lead to increased rule base complexity.
The bar chart in Figure 12 illustrates research trends in
employing FL to address nonlinearities and uncertainties
in EH, ACS, and RAS domains over five two-year peri-
ods from 2014 to 2023. The research activity for ACS
remains relatively stable, which is likely due to its estab-
lished technologies and incremental improvements that
reduce the need for frequent new studies. In contrast,
EH and RAS show notable increases in research activity.
For EH, the growing focus on renewable energy solutions
and the need to optimize energy systems drive signifi-
cant research interest. RAS, on the other hand, experi-
ences the highest increase due to rapid advancements
in robotics and autonomous technologies, which contin-
uously present new challenges and opportunities for FL
applications. Moreover, research in RAS and EH is driven
by strong incentives from high-growth industries such
as robotics, automation, and renewable energy, which
attract substantial investments. The dynamic evolution
in these fields fuels ongoing research to enhance perfor-
mance, robustness, and adaptability, making them prime
areas for innovative FL applications.
Figure 13 compares the types of FL used across the
application domain. In the domain of EH, Mamdani FL is
predominantly used, with a significantly higher number
of studies compared to T-S FL. This suggests a preference
for Mamdani FL in optimizing and controlling EH sys-
tems, likely due to its intuitive rule-based approach that
suits the nonlinear characteristics of energy systems. For
ACS, Mamdani FL also shows a clear dominance, with only
a few studies utilizing T-S FL. In the RAS domain, while
Mamdani FL remains the most widely used, T-S FL also has
a substantial presence. The higher number of studies in
RAS reflects the dynamic and evolving nature of robotics
and autonomous systems, which continuously present
new challenges. One main reason for the significant num-
ber of T-S FL studies in RAS is its application in closed-loop
systems, where FL is often hybridized with other types
of controllers such as PID and SMC. This hybrid approach
leverages the strengths of T-S FL in handling complex sys-
tems with sophisticated mathematical modelling, offer-
ing greater precision and adaptability in conjunction with
traditional control methods.
Overall, the chart in Figure 13 highlights the strong
preference for Mamdani FL across all three domains, with
T-S FL gaining notable traction in the rapidly advancing
field of RAS. This distribution underscores the versatil-
ity and applicability of both FIS in addressing various
nonlinearities and uncertainties in different technological
contexts.
The pie charts in Figure 14 illustrate how FL is modi-
fied or hybridized with other control methods to improve
the performance compared to the heuristic method to
address perturbations in the three application domains.
The heuristic method, which relies on expert knowledge
24 H. H. TANG AND N. S. AHMAD
Figure 12. Trends in the number of studies on employing FL to address nonlinearities and uncertainties in EH, ACS, and RAS domains,
as reported in the literature from 2014 to 2023.
Figure 13. Comparison of FIS used across EH, ACS and RAS domains.
and empirical data to set FL parameters, serves as a base-
line for comparison and does not require major modifica-
tions.
In the EH domain, heuristic methods are used in
30.77% of the studies, indicating a substantial reliance
on expert knowledge. However, 69.23% of the stud-
ies utilize various modifications to enhance FL perfor-
mance. SI methods, such as GA and PSO, are the most
prevalent at 33.33%, showing the importance of opti-
mization techniques in improving system efficiency. SMC
(10.26%), adaptive methods (7.69%), hybridizations with
P&O (7.69%), and H∞control (5.13%) are also employed,
highlighting the need for robust and adaptive control
strategies in energy systems.
With regard to ACS, heuristic methods dominate at
65.22%, reflecting a strong preference for straightfor-
ward, reliable control strategies based on expert knowl-
edge. The remaining 34.78% of studies employ other
modifications, including NN (13.04%) to enhance learning
capabilities, PID controllers (8.70%) for precise control,
and SI methods (8.70%) for optimization. This indicates
that while heuristic methods are preferred, there is still a
significant interest in integrating advanced techniques to
improve performance.
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 25
Figure 14. Distribution of FL modifications and hybridizations to improve the control strategy across EH, ACS and RAS domains. (a) EH.
(b) ACS and (c) RAS.
In the RAS domain, the diversity of modifications is
much greater, with heuristic methods used in only 12.12%
of the studies. PID controllers (18.18%) and SI tuning
methods (15.15%) are the most common, demonstrat-
ing the need for flexible and precise control in com-
plex robotic systems. Adaptive methods (12.12%), SMC
(12.12%), TDC (12.12%), and NN (9.09%) are also widely
used, emphasizing the importance of hybrid approaches
in addressing the challenges of nonlinearities and uncer-
tainties. MPC (3.03%) is utilized for its predictive capabil-
ities, and DWA (3.03%) is used for local path planning,
which are crucial for dynamic and uncertain environ-
ments.
Thus, Figure 14 shows that while heuristic methods
are prevalent in ACS due to their simplicity and relia-
bility, more advanced modifications and hybridizations
are frequently employed in EH and RAS. This reflects
the need for more sophisticated approaches to opti-
mize performance and manage the complexities in these
domains. In addition, automated search techniques for
optimizing membership functions via SI offer the poten-
tial to overcome the limitations of the heuristic method.
By reducing the reliance on extensive user interven-
tion and expertize, these approaches can lead to more
adaptive and robust fuzzy systems capable of handling
complex, dynamic environments with greater precision.
This ongoing research highlights the potential for signif-
icant advancements in FL, making it more accessible and
effective for a wider range of applications.
Unlike EH and ACS, where the types of nonlinearities
and uncertainties are relatively consistent across studies,
the perturbations addressed in the RAS domain exhibit
significant diversity. The radar chart in Figure 15 visu-
alizes the number of studies against sources of pertur-
bations in various RAS as documented in Table 4.The
chart is segmented into several key perturbation cate-
gories, including deadzone, saturation, unknown dynam-
ics, parameter variations, load, external noise, environ-
mental changes, internal disturbances, and external dis-
turbances. In this plot, the internal disturbances cate-
gory encompasses issues such as backlash, wear, friction,
and hysteresis, while the external disturbances category
includes uncertain disturbances originating outside the
system in the context of motion control, as well as uncer-
tain driving conditions in the context of ACC. Each axis
represents one of these perturbations, with the radial dis-
tance indicating the number of studies that applied FL to
address the specific perturbation.
26 H. H. TANG AND N. S. AHMAD
Figure 15. Common sources of perturbations in RAS addressed using FLC: Number of reported studies over the past decade.
Tab le 6 . Potential Research Directions for Enhancing FL Control Performance in EH, ACS and RAS Domains.
Domain Focus Area Research Strategies
EH Enhanced ComputationalEfficiency Develop lightweight AI algorithms that can balance computational load with rapid real-time
response in MPPT.
Investigate advanced hardware accelerators for faster processing of FL.
System Integration and Modeling Create comprehensive modelling frameworks for integrating multiple components within
cohesive FLC frameworks.
Explore modular and scalable design approaches to manage system complexity.
Advanced Optimization Techniques Develop novel optimization methods for fine-tuning FL rules and membership functions,
reducing expertize for initialization strategies.
Investigate adaptive and learning-based optimization techniques for real-time adjustment of
FL parameters.
Enhanced Prediction Accuracy Explore hybrid approaches combining FL with machine learning algorithms to further improve
prediction accuracy and efficiency in energy management
ACS Scalable System Design Research scalable methodologies for large-scale ACS, ensuring efficient control across extensive
sensor and actuator networks.
Develop hierarchical or distributed control systems for managing complexity.
Improved Sensor Data Handling Innovate robust data fusion techniques for handling uncertainties in sensor measurements.
Design fault-tolerant control systems maintaining performance despite sensor failures.
Adaptive Control Strategies Explore adaptive control strategies for HVAC and greenhouse settings, minimizing recalibration
needs.
Investigate machine learning techniques for real-time adaptation to environmental changes.
RAS Precision Control for High-Speed
Dynamics
Develop high-precision FL controllers for agile robotics and autonomous vehicles.
Research hybrid control approaches combining FL with other methods for enhanced precision
and stability.
Managing Rule Base Complexity Create automated methods to simplify and optimize rule bases as systems scale.
Investigate hierarchical FL systems for breaking down complex tasks into manageable sub-
tasks.
Stability Analysis and Modeling Conduct in-depth stability analysis for FL-controlled systems to derive clear stability conditions.
Explore robust mathematical modelling frameworks capturing FL system dynamics and inter-
actions.
Preventing Overfitting in FL Systems Develop generalized FL models for consistent performance across diverse scenarios.
Investigate cross-validation techniques for ensuring robustness and generalization of FL
systems.
Multi-objective optimization Utilize multi-objective optimization strategy to reconcile conflicting objectives in FLC sys-
tems such as internal stability and transient response to ensure optimal performance while
maintaining robustness against uncertainties.
SYSTEMS SCIENCE & CONTROL ENGINEERING: AN OPEN ACCESS JOURNAL 27
Based on the chart, the robotic manipulators (blue
line) shows the highest number of studies addressing
deadzone and unknown dynamics, highlighting signifi-
cant challenges in these areas. Mobile robots (orange line)
frequently encounter external disturbances and environ-
mental changes, as indicated by the relatively larger area
covered in these categories. Cable-driven robots (yellow
line) show a balanced distribution across various per-
turbations, with noticeable emphasis on handling exter-
nal disturbances and environmental changes. ACC sys-
tems (purple line) predominantly address external distur-
bances and external noise, reflecting the critical impor-
tance of these factors in automotive applications. Lastly,
motors (green line) have fewer studies overall, with a
focus on handling internal disturbances and saturation.
In summary, the chart in Figure 15 provides a com-
prehensive overview of the prevalent challenges in dif-
ferent RAS and the extent to which FL has been applied
to address these issues. This visual representation under-
scores the areas where further research and development
are necessary to improve system robustness and perfor-
mance.
5.2. Potential research directions
Based on the previous analyses, several open problems
and potential research directions emerge in the applica-
tion of FL to address nonlinearities and uncertainties in
EH, ACS and RAS domains as discussed in Table 6.
6. Conclusion
In conclusion, this state-of-the-art review paper has
delved into the multifaceted applications of FL across EH,
ACS and RAS. Through meticulous scrutiny of existing lit-
erature, it has illuminated FL’s remarkable versatility in
navigating nonlinearities and uncertainties within diverse
technological domains. By contrasting the evolving land-
scape of FL’s utilization in EH and RAS with the enduring
interest in ACS applications, this review underscores the
dynamic nature of FL’s relevance. Evaluation of various FIS
across domains has provided nuanced insights, facilitat-
ing informed decision-making for researchers and practi-
tioners. Furthermore, the exploration of advanced modifi-
cations and hybridizations of FL, coupled with the identi-
fication of open problems and future research directions,
sets a solid foundation for the continued advancement
of FL technology. This comprehensive review not only
encapsulates the current state of FL applications but also
lays out a roadmap for its future evolution, promising sig-
nificant contributions towards addressing complexities in
modern systems.
Nomenclature
Acronyms Descriptions
FL, FLC Fuzzy logic, Fuzzy logic controller
AFLS Adaptive FL system
FOFLC Fractional order FLC
AI Artificial intelligence
FIS Fuzzy inference system
SI Swarm intelligence
T-S, M Takagi-Sugeno, Mamdani
GA Genetic algorithm
PSO Particle swarm optimization
BA Bat algorithm
HSA Harmony search algorithm
FFO Farmland fertility optimization
UI Uniform irradiance
PS Partial shading
LFC Load frequency controller
ACE Area control error
CS, FS Compressor speed, fan speed
PMSG Permanent magnet synchronous gener-
ator
DC, BLDC Direct current, brushless DC
NN, ANN Neural network, artificial neural network
NNARX Auto-regressive neural network with
external inputs
P, PD, PI, Proportional, Proportional-derivative,
Proportional-integral
PID Proportional-integral-derivative
SMC Sliding mode control
MPC Model predictive control
BST Backstepping
CNF Composite nonlinear feedback
FCC Friction compensation controller
TDC, TDE Time-delay control, time-delay estima-
tion
FBC Feedback linearization controller
PØ Perturb Observe
DoF Degree of freedom
EH Energy harvesting
ACS Ambient conditioning systems
RAS Robotics and autonomous systems
MPP, MPPT Maximum power point, MPP tracking
PV, WT Photovoltaic, wind turbine
HVAC Heating, Ventilation, and Air Condition-
ing
VR, HR Ventilation rate, heating rate
HuR, DHuR Humidification rate, dehumidification
rate
GH Greenhouse
AC Air conditioning
IPA Interior point algorithm
RM Robotic manipulator
28 H. H. TANG AND N. S. AHMAD
WMR, DWMR Wheeled mobile robot, Differential WMR
AV Autonomous vehicle
UUV, UAV Unmanned underwater vehicle, unman
ned aerial vehicle,
DWA Dynamic window approach
RRT Rapidly-exploring random tree
ACC Adaptive cruise control
Authors’ contributions
Data curation, H.H.T; Design, H.H.T; Formal analysis, H.H.T.
and N.S.A; Funding acquisition, N.S.A.; Investigation, H.H.T
and N.S.A; Methodology, H.H.T; Project administration,
N.S.A.; Software, H.H.T; Supervision, N.S.A.; Validation,
H.H.T. and N.S.A.; Writing–original draft, H.H.T; Writ-
ing–review & editing, N.S.A.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data supporting the findings of this study are available from the
corresponding author, N.S.A, upon reasonable request follow-
ing the publication of the paper.
Funding
This work was supported by the Malaysia Ministry of Higher Edu-
cation under Fundamental Research Grant Scheme with Project
Code: FRGS/1/2024/TK07/USM/02/3, and Universiti Sains Malay
sia under USM-Industry Matching Grant Scheme with Project
Code: 2023/0254/EM0023.
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