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Vehicle Fuzzy Controller Design Using Imperialist Competitive Algorithm

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In this paper a novel socio-politically inspired optimization strategy, an Imperialist Competitive Algorithm, is applied to the problem of designing a fuzzy controller. The goal is to design a controller to enhance the transient and steady state behaviour of the system output. Having fixed the rule base of the fuzzy system, the controller is designed through determining the membership functions of the input and output variables. The design method is applied to a model of vehicle. The inputs of the fuzzy system are velocity of the vehicle and the sloop of the road. The fuzzy controller controls the speed of the vehicle by adjusting the amount of gas into vehicle engine. Comparison results among the designed controller and the controller designed by the expert show that the controller obtained by Imperialist competitive algorithm has better performance than the expert controller.
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Vehicle Fuzzy Controller Design
Using Imperialist Competitive Algorithm
Ashkan MohammadZadeh Jasour1, Esmaeil Atashpaz Gargari2, Caro Lucas2
1Iran University of Science and Technology, Tehran, Iran
2Control and Intelligent Processing Centre of Excellence, Electrical and Computer Engineering School of
University of Tehran, Tehran, Iran
amjasour@ee.iust.ac.ir , e.atashpaz@ece.ut.ac.ir , lucas@ut.ac.ir
Abstract: In this paper a novel socio-politically
inspired optimization strategy, an Imperialist
Competitive Algorithm, is applied to the problem of
designing a fuzzy controller. The goal is to design a
controller to enhance the transient and steady state
behaviour of the system output. Having fixed the rule
base of the fuzzy system, the controller is designed
through determining the membership functions of the
input and output variables. The design method is
applied to a model of vehicle. The inputs of the fuzzy
system are velocity of the vehicle and the sloop of the
road. The fuzzy controller controls the speed of the
vehicle by adjusting the amount of gas into vehicle
engine. Comparison results among the designed
controller and the controller designed by the expert
show that the controller obtained by Imperialist
competitive algorithm has better performance than the
expert controller.
Keywords: Fuzzy Controller, Imperialist
Competitive Algorithm (ICA), Membership
Function, Vehicle
1 Introduction
Fuzzy controllers, as nonlinear controllers,
represent successful implementation of fuzzy logic
in practical control problems. These controllers,
based on fuzzy logic, simulate the behaviour of
experts in controlling the system. Unlike their
classical counterparts, fuzzy controllers do not
require a precise mathematical model of the
systems and using the expert knowledge they
construct their rule base [1,2]. One of the
disadvantages of fuzzy controllers is that, they lack
the ability of learning. Hence they are strongly
dependent to the expert knowledge. A learning
strategy can cope with this problem and automate
the controller design process through adding the
ability of learning and modifying control rules [3].
In this paper the problem of designing a fuzzy
controller is stated in the form of an optimization
problem. The optimal fuzzy controller is
determined through finding the membership
function of the variables, using a novel global
search strategy, called Imperialist Competitive
Algorithm (ICA). ICA is a global search strategy
that uses the socio-political competition among
empires as a source of inspiration. Like other
evolutionary ones that start with initial population,
ICA begins with initial empires. Any individual of
an empire is a country. There are two types of
countries; colony and imperialist state that
collectively form empires. Imperialistic
competitions among these empires form the basis
of the ICA. During this competition, weak empires
collapse and powerful ones take possession of their
colonies. Imperialistic competition converge to a
state in which there exist only one empire and its
colonies are in the same position and have the
same cost as the imperialist[4].
The novel evolutionary optimization algorithm,
ICA, has extensively been used to solve different
kinds of optimization problems. In [5] and [6] ICA
is used to design a PID controller for a SISO
system and for characterization of materials
property from sharp indentation test. In [7] ICA is
used for reverse analysis of an artificial neural
network in order to characterize the properties of
materials from sharp indentation test. In order to
find the optimal priorities for each user in
recommender systems, [8] uses ICA in “Prioritized
user-profile” approach to recommender systems,
trying to implement more personalized
recommendation by assigning different priority
importance to each feature of the user-profile in
different users. In [9] ICA is used for Gershgorian
band shrinking and decentralized PID controller
design for MIMO systems.
In this paper, ICA is applied to the problem of
designing a fuzzy controller for a vehicle
dynamics. The controller obtained by ICA is
compared with that of expert.
In the following of the paper, section 2 states the
problem of designing fuzzy controller for a vehicle
dynamics. Section 3 briefly introduces ICA and in
section 4, ICA is applied to design a fuzzy
controller for the vehicle dynamics. Eventually
conclusion of the paper and remarks to the future
are given in section 5.
2 Fuzzy Control of Vehicle
Figure 1 shows a typical model of vehicle
dynamics and different force applied to it.
Figure 1: Model of vehicle with forces.
Mathematical model of vehicle shown in figure 1
is as following [10]:
argu ffff
dt
xd
m=)( 2
2
(1)
Where, m is the mass of vehicle, u
fis the input
force to vehicle from the engine, g
fis grade
resistance that arise from slop of road, r
f is rolling
resistance of tires, a
f is aerodynamic resistance
and is the position of vehicle. Mentioned forces
are as following.
)( dt
dx
kf rr =, 2
)( dt
dx
kf aa =,
θ
sinmgfg=
Tkf uu .= (2)
Where
θ
is the slope of the road and T is the
amount of input gas to engine. Vehicle model in
state space is in the following form:
2
222
21
sin X
m
k
X
m
k
gT
m
k
X
XX
aru =
=
θ
&
&
(3)
States 1
X and 2
Xare position and velocity of the
vehicle, respectively. The bound for velocity of
vehicle (V) is [0 100] and for
θ
is [-10 10] and for
T is [0 10].
The goal is to design a fuzzy controller which
automatically sets the vehicle velocity to 50 mph,
for different initial speed and slop of the road. The
fuzzy controller controls the amount of input gas to
the vehicle engine by adjusting T, with respect to
slop of the road (
θ
) and current speed of vehicle
(V).
To design such a controller, the vehicle’s velocity
is considered to have membership in three sets,
namely Low, Medium, High. The slope has
membership in three sets Down, Level, Up. The
output of the fuzzy system, T, has membership in
five sets, Very Low, Low, Medium, High, Very
High. There are nine fuzzy if-then rule in the
below form.
IF V = MFV
i & θ = MFθ
j THEN T = MFT
k
13,1 3,1 5ijk
≤≤≤≤ (4)
Setting the fuzzy rules by considering expert
knowledge, the membership functions are found by
applying a novel global search strategy, ICA.
3 Brief Description of Imperialist Competitive
Algorithm
Imperialism is the policy of extending the power
and rule of a government beyond its own
boundaries. A country may attempt to dominate
others by direct rule or by less obvious means such
as a control of markets for goods or raw materials.
The latter is often called neo-colonialism [4]. ICA
is a novel global search heuristic that uses
imperialism and imperialistic competition process
as a source of inspiration. Figure 2 shows the
pseudo code for this algorithm. This algorithm
starts with some initial countries. Some of the best
countries are selected to be the imperialist states
and all the other countries form the colonies of
these imperialists. The colonies are divided among
the mentioned imperialists based on their power.
After dividing all colonies among imperialists and
creating the initial empires, these colonies start
moving toward their relevant imperialist state. This
movement is a simple model of assimilation policy
that was pursued by some imperialist states [4].
Figure 2: Pseudo code for the proposed algorithm.
Figure 3 shows the movement of a colony towards
the imperialist. In this movement,
α
and x are
random numbers with uniform distribution as
illustrated in equation (5) and d is the distance
between colony and the imperialist.
),(
),0(
γγα
β
=
×=
U
dUx (5)
Where β and γ are parameters that modify the area that
colonies randomly search around the imperialist. In our
implementation β and γ are 2 and 0.5 (Rad)
respectively.
Figure 3: Motion of colonies toward their relevant
imperialist.
The total power of an empire depends on both
the power of the imperialist country and the power
of its colonies. In this algorithm, this fact is
modeled by defining the total power of an empire
by the power of imperialist state plus a percentage
of the mean power of its colonies.
In imperialistic competition, all empires try to
take possession of colonies of other empires and
control them. This competition gradually brings
about a decrease in the power of weaker empires
and an increase in the power of more powerful
ones. This competition is modeled by just picking
some (usually one) of the weakest colonies of the
weakest empires and making a competition among
all empires to possess these (this) colonies. Figure
4 shows a big picture of the modeled imperialistic
competition. Based on their total power, in this
competition, each of empires will have a likelihood
of taking possession of the mentioned colonies.
The more powerful an empire, the more likely it
will possess these colonies. In other words these
colonies will not be certainly possessed by the
most powerful empires, but these empires will be
more likely to possess them.
Any empire that is not able to succeed in
imperialist competition and can not increase its
power (or at least prevent decreasing its power)
will be eliminated.
The imperialistic competition will gradually
result in an increase in the power of great empires
and a decrease in the power of weaker ones. Weak
empires will lose their power gradually and
ultimately they will collapse. The movement of
colonies toward their relevant imperialists along
with competition among empires and also collapse
mechanism will hopefully cause all the countries to
converge to a state in which there exist just one
empire in the world and all the other countries are
its colonies. In this ideal new world colonies have
the same position and power as the imperialist.
1) Form the array of variables to be
found optimally.
2) Select some random points on the
function and initialize the empires.
3) Move the colonies toward their
relevant imperialist (Assimilating).
4) If there is a colony in an empire
which has lower cost than that of
imperialist, exchange the positions
of that colony and the imperialist.
5) Compute the total cost of all empires
(Related to the power of both
imperialist and its colonies).
6) Pick the weakest colony (colonies)
from the weakest empires and give it
(them) to the empire that has the
most likelihood to possess it
(Imperialistic competition).
7) Eliminate the powerless empires.
8) If there is just one empire, stop, if
not go to 2.
Figure 4: Imperialistic competition: The more powerful
an empire is, the more likely it will possess the weakest
colony of weakest empire.
4 Fuzzy Controller Design Using ICA
To apply the Imperialist Competitive Algorithm
(ICA) to the problem of designing fuzzy controller,
the parameters of the membership functions are
coded to form the array country and a cost function
is defined in such a way that the design criteria are
satisfied through minimizing it.
Figure 5 shows the typical membership functions
of input variable V in three relevant sets. Three sets
can uniquely be specified by three points P1, P2,
P3.
Figure 5: typical membership functions of input
variable V
Figure 6: typical membership functions of input
variable θ
Figure 7: typical membership functions of input
variable T
The same can be done for input variable θ, shown
in figure 6. The three MFs of this variable can be
specified by three points P4, P5, P6. Finally,
considering the figure 7 for output variable T, the
five MFs of this variable can be represented by
five points P7, P8, P9, P10, P11. Hence, the problem
of finding the MFs is reduced to the problem of
determining 11 points ( i,1 i 11≤≤P). The 11 points
are put together to form the array country.
Country = [P1, P2, P3, P4, P5, P6, P7, P8, P9,
P10, P11]
The transient and steady state response of the
system is used to evaluate the performance of the
designed fuzzy controller. In the this paper, the
transient characteristics of the output Rise Time
(tr), Overshoot (MP), Settling Time (ts) along with
the integral of absolute error (IAE) are used to
evaluate the designed controller. A good controller
results in the output to have low values for tr, MP
and ts. The multi-objective design problem is
converted to single objective one by considering a
linear combination of all criteria. Therefore
Cost Function = w1 . tr + w2 . MP + w3 . ts
+ w4 . IAEs (6)
wis are the weights which must be determined by
designer. The ICA looks for the best country array
(country*). That is the array of Pi's, whose
relevant cost function is minimal.
{
min
= arg Cost FunctionCountry (7)
5 Results
In this part the Imperialist Competitive Algorithm
(ICA) is used to design a fuzzy controller for the
vehicle dynamics. The parameters of vehicle are
1=
m
ku, 1.0== m
k
m
kar . In ICA, initial number of
countries is set to 100, 10 of which are chosen as
the initial imperialists. Also β and γ are set to 2 and
0.5 (Rad) respectively. The maximum iterations of
the ICA is set to 100 and it reached to the total cost
of 110 in these iterations. Figure 8 depicts the
minimum and mean cost of ICA versus iteration.
Obtained Fuzzy membership functions using ICA
are shown in figures 9 to 11. Fuzzy rules obtained
by expert are given in Table 1.
Figure 8: depicts the minimum and mean cost of ICA
versus iteration
Figure 9: membership functions of input variable V
Figure 10: membership functions of input variable
θ
Figure 11: membership functions of input variable T
Table 1: Fuzzy Rules Obtained by Expert
UP LEVEL DOWN
θ
V
HIGH HM HM LOW
HM Medium LM OK
LM LM LOW HIGH
Results for both ICA fuzzy controller and expert
fuzzy controller for different initial conditions are
shown in figure 12 to 15.
Figure 12. Vehicle Velocity for V(0)=0 &
θ
(0) =5
Figure 13: Vehicle Velocity for V(0)=0 &
θ
(0) =1
Figure 14: Vehicle Velocity for V(0)=100 &
θ
(0) =7
Figure 15: Vehicle Velocity for V(0)=100 &
θ
(0) =-6
As shown in result figures, the controller designed
by ICA has better response in comparison to expert
controller. The mean values of each term of cost
function for different initial conditions for both
controllers are compared in Table 2.
Table 2: Cost function terms for controllers
Criteria
Method tr ts MP Ess
ICA 7.8 10.4 0 1.98
Expert 8.1 13.1 0 3.1
According to Table 2, fuzzy controller obtained by
ICA has a better performance in all criteria's
considered in cost function, in comparison with
one designed by expert.
6 Conclusion
In this paper a novel global optimization
algorithm called Imperialist Competitive
Algorithm (ICA), is presented for fuzzy controllers
design. ICA is a novel global search heuristic that
uses imperialism and imperialistic competition
process as a source of inspiration. Using this
algorithm, membership functions for fuzzy
controllers, are determined. The proposed design
method is applied to a model of vehicle. In this
problem fuzzy controller was used to control the
speed of the vehicle by adjusting the amount of gas
into vehicle engine. Comparison results among the
designed controller by ICA and the controller
designed by the expert showed that the controller
obtained by Imperialist competitive algorithm has
better performance than the expert controller.
Future works in this area include design of entire
fuzzy controllers including fuzzy rules and
membership functions using ICA.
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... Evolutionary algorithms, such as genetic algorithm "( Wu and Chau, 2006;Muttil and Chau, 2006)" particle swarm optimization "( Kennedy and Eberhart, 1995;Lei et al., 2006)", taboo search "( Battiti and Tecchiolli, 1992;de Werra and Hertz, 1989;Hertz, 1992)" ant colony optimization " (Blum and Sampels, 2002;Parpinelli et al., 2002;Ying and Jianying, 2000)" bees algorithm " (Pham et al., 2006a)" and " (Pham et al., 2006b)" and " (Pham et al., 2006c)" and simulated annealing "( Cardoso et al., 1997;Rosen and Goodwin, 1997)" are a set of algorithms that are introduced and suggested in the past decades for solving optimization problems in different science and engineering fields. Imperialist Competitive Algorithm (ICA) is an algorithm introduced for the first time in 2007 by "Atashpaz-Gargari and Lucas (2007)" and used for optimizing inspired by the imperialistic competition and has a considerable relevance to several engineering applications " (Biabangard-Oskouyi et al., 2008;Jain and Nigam, 2010;Jasour et al., 2008;Khabbazi et al., 2009;Lucas et al., 2010;Sepehri Rad and Lucas, 2008;Zhang et al., 2009)". Like other evolutionary ones, the proposed algorithm starts with an initial population. ...
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Recently, Colonial Competitive Algorithm (CCA) has proven its superior capabilities, such as faster convergence and better global minimum achievement in optimization problems. In this paper, CCA is utilized to optimize the coefficients of a decentralized PID controller for a MIMO evaporator system. The optimization criterion is considered as the Integral Absolute Error (IAE) to minimize the tracking error. As the first step, the evaporator's three input-three output transfer matrix is identified using measured dataset based on the prediction error model method. In order to design decentralize controllers, input-output pairing is performed based on the relative Gain Array method. Decentralized PID controllers are then designed using Ziegler-Nichols, Genetic Algorithm, and the proposed CCA techniques. The simulation results verify the superiority of CCA to the Ziegler-Nichols and Genetic Algorithm tuning techniques for decentralized PID controllers.
Conference Paper
In this paper, a colonial competitive algorithm is applied to the problem of designing a multivariable PID controller. The goal is to design a controller to decouple the controlled process, and to track the desired inputs by outputs of the process as much as possible. The method is used to design a multi variable controller for a typical distillation column process. Also a GA and an analytical method are used to design the controller parameters. Comparison results among these methods show that the controller obtained by colonial competitive algorithm has better performance than the others.
Conference Paper
This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.
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Included in Prentice Hall's MATLAB Curriculum Series, this text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.
The fuzzy logic controller (FLC) provides a means of converting a linguistic control strategy. A survey of the FLC is presented, and a general methodology for constructing an FLC and assessing its performance is described. In particular, attention is given to fuzzification and defuzzification strategies, the derivation of the database and fuzzy control rules, the definition of fuzzy implication, and an analysis of fuzzy reasoning mechanisms
Application of Imperialistic Competition Algorithm in Recommender Systems
  • H Sepehri Rad
  • C Lucas
Sepehri Rad, H., Lucas, C. "Application of Imperialistic Competition Algorithm in Recommender Systems", 13th Int'l CSI Computer Conference (CSICC'08), Kish Island, Iran,2008.