Radu-Emil Precup

• Prof. Dr.-Ing.
• Professor at Polytechnic University of Timişoara

The current paper proposes to mix two data-driven algorithms, i.e. Model-Free Control (MFC) and Fictitious Reference Iterative Tuning (FRIT). The MFC algorithm has the main advantage of not using the precise process model, and the FRIT algorithm has the main benefit of determining the optimal parameters of the controller using a set of initial inpu...

This article presents an adaptive neural network control scheme with prescribed performance for stochastic nonlinear systems. Unlike existing adaptive stochastic control schemes that primarily utilize deterministic neural networks for approximations in complex stochastic environments, we employ stochastic neural networks to approximate the stochast...

An n-DOF mechatronic systems with uncertain dynamics, mismatched disturbances, and input amplitude and rate saturations can hardly be modeled well such that model-based control approaches become infeasible. In this paper, a model-independent solution only using input-output data of mechatronic system is innovatively provided. Together with a fracti...

This paper proposes the use of metaheuristic optimization algorithms to tune the Proportional-Derivative (PD) learning rules within the framework of Iterative Learning Control applied to low-cost Takagi-Sugeno Proportional-Integral (PI)-fuzzy controllers for tower crane system payload position control. Four PD learning rules are considered: direct...

Iterative Feedback Tuning (IFT) is one of the earliest algorithms from the data-driven class that was pioneered by Hjalmarsson et al. The current book chapter aims to propose and validate via experiments the IFT algorithms on the Two Rotor Aerodynamic System (TRAS) laboratory equipment for Single Input–Single Output (SISO) control structure. Theref...

In the context of diagnostic and abduction, this chapter is built upon the meta-hypothesis stating that the true cause of the observation (referred to as the concept) belongs to a set of explanations. In addition, finding the concept means a strategy of observation planning (referred to as the policy). A tensor-based approach to causal network mode...

This paper proposes a combination of a data-driven algorithm represented by the second-order continuous-time Active Disturbance Rejection Control (ADRC) and a Sliding Mode Control (SMC) algorithm. The purpose of this hybrid controller referred to as ADRC-SMC is to improve the overall control-loop system performance while guaranteeing its stability....

Composite adaptive control integrates direct and indirect adaptive control to achieve asymptotic convergence of both tracking errors and prediction errors while maintaining the global stability of the closed-loop system [1] . To explain the smooth behavior of parameter estimation in composite adaptation, it is convenient to interpret the composit...

Updated free eprint link for the first 50 readers for the next and final version of the manuscript: https://www.tandfonline.com/eprint/KINZGAQNEQIET5RCMKWT/full?target=10.1080/00207721.2023.2293486 Fuzzy control has become one of the most effective tools for dealing with complex engineering processes. Over the years, research on fuzzy control syst...

Deadline extended Call for Papers Special Issue on Machine Learning and Big Data in Control Systems in the Control Engineering and Applied Informatics (CEAI) journal, http://ceai.srait.ro Intelligent control is based on the synergistic combination of advances in Internet of Things (IoT), Crowdsensing technologies, Big Data (BD) analytics, Mobile a...

Mechatronic systems have become an integral part of the daily live, found everywhere from aircraft and spacecraft to homes and automobiles. With increasing demands for high performance, reliability, and affordability, there is a growing need to make these systems safer, greener, and smarter. As a result, there has been a surge of research interest...

This paper applies three classical and very popular discrete-time model-based sliding mode controllers, namely the Furuta controller, the Gao controller, and the quasi-relay controller due to Milosavljević, to the position control of tower crane systems. Three single input-single output (SISO) control systems are considered, for cart position contr...

This paper introduces a novel reference tracking control approach implemented using a combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf Optimizer (GWO) algorithm. The classical neural network (NN)-based implementation of the Critic, optimized with the Gradient Descent (GD) algorithm, is replaced with the GWO al...

The goal of this paper is to obtain optimal models of an unstable transport system, which is a nonlinear process represented by the two-wheeled unstable transport system. An optimization problem is defined in order to ideally minimize and practically reduce the differences of the outputs of the real-time laboratory equipment with respect to the out...

This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning o...

This paper suggests four estimation approaches, namely two linear and two nonlinear ones: Extended Luenberger State Observer (ELSO), Kalman Filter Observer (KFO), Extended Kalman Filter Observer (EKFO) and Sliding Mode Observer (SMO), for electric drive systems (EDSs) with direct current (DC) motor. The application considered in this paper is a sim...

This paper proposes two nonlinear estimation approaches, namely based on Extended Kalman Filter (EKF) and a Takagi-Sugeno Fuzzy Observer with 32 rules (TSFO-32), for a Strip Winding System (SWS) characterized by variable reference input, variable moment of inertia with constant increasing tendency and variable parameters. The SWS is a complex and n...

An approach to the Colored Petri Nets (CPN)-based control is proposed in this paper. CPN are used for modeling the dynamics of both the controller and the controlled process in the control system structure. The mathematical model of the controlled process is discretized in order to use CPN in modeling the controlled process and the control system a...

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free...

A big volume of new data is generated in every moment of the day by different devices and domains as social network, mobile and desktop devices, financial transaction, online websites, different search engines and a lot of smart home devices. The generated data is diversified and can be structured or unstructured. Clustering is the process of categ...

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm is applied to improve the performances of three proportional-integral (PI) controllers by solving an optimization problem. The IFT algorithm is validated using experiments...

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position...

The purpose of this paper is to design the optimal controllers for nonlinear processes with Shape Memory Alloy (SMA) wire actuators viewed as controlled processes. Optimal process models are first derived. A comparative analysis is done between the evolved Takagi-Sugeno-Kang (TSK) fuzzy models of SMA wire actuators, two linear dynamic system models...

This book categorizes the wide area of data-driven model-free controllers, reveals the exact benefits of such controllers, gives the in-depth theory and mathematical proofs behind them, and finally discusses their applications. Each chapter includes a section for presenting the theory and mathematical definitions of one of the above mentioned algor...

This paper presents a novel Reinforcement Learning (RL)-based control approach that uses a combination of a Deep Q-Learning (DQL) algorithm and a metaheuristic Gravitational Search Algorithm (GSA). The GSA is employed to initialize the weights and the biases of the Neural Network (NN) involved in DQL in order to avoid the instability, which is the...

This paper presents a new Reinforcement Learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs). Due to an efficient tradeoff to exploration and exploitation, the GWO algorithm shows good results in NN training and solving complex optimizatio...

We organize a Special Issue on “Emerging Control and Automation Technologies for Advanced Mechatronic Systems” in the IFAC journal Control Engineering Practice. Welcome your submission! More details can be found at: https://www.journals.elsevier.com/control-engineering-practice/call-for-papers/call-for-papers-special-issue-on-emerging-control-and...

Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better un...

This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to the optimal tuning of interval type-2 fuzzy controllers. Inserting the information feedback model F1 in SMA leads to a new version of the metaheuristic algorithm, further referred to as SMAF1. The paper discusses implementation details specific to interval t...

This paper proposes two applications of nature-inspired optimization algorithms (NIOAs) to solve a path planning problem and the optimal tuning of Proportional-Integral (PI)-fuzzy controllers as tracking controllers for nonholonomic wheeled mobile robots in static environments. Two optimization problems are solved by NIOAs and included first in an...

The paper presents the application of the tensor product (TP)‐based model transformation technique to model and control the cart position of single‐input multi‐output pendulum‐cart systems (PCSs). The modeling is first carried out. The derived TP model, the nonlinear model of PCS, and the laboratory equipment are tested in the same open‐loop scenar...

This paper presents the application of the tensor product (TP)‐based model transformation approach to produce Tower CRrane (TCR) systems models. The modeling approach starts with a nonlinear model of TCR systems as representative multi‐input–multi‐output controlled processes. A linear parameter‐varying model is next derived, and the modeling steps...

This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tun...

This paper proposes as an element of novelty the Unified Form (UF) clustering algorithm, which treats Fuzzy C-Means (FCM) and K-Means (KM) algorithms as a single configurable algorithm. UF algorithm was designed to facilitate the FCM and KM algorithms software implementation by offering a solution to implement a single algorithm, which can be confi...

Trajectory tracking of unmanned aerial vehicles (UAVs) has been studied nowadays because it is necessary to design new controllers under different conditions. Severe atmospheric conditions are one of the major problems to overcome according to the path of the UAV. Conditions such as wind speed that can vary according to the weather conditions can a...

The goal of this paper is to develop the optimal fuzzy controllers for nonlinear processed represented by electromagnetic actuated clutch systems in order to improve the control system performance. A Takagi-Sugeno type-1 fuzzy logic controller and a Takagi-Sugeno type-2 fuzzy logic controller are designed here to enhance position tracking performan...

This paper proposes an approach to the tuning of model-free controllers for the midcarpal joint angles, which are important finger angles that ensure the desired finger dynamics in prosthetic hand myoelectric-based control systems. The process in these control systems is characterized by fuzzy models that operate with myoelectric signals obtained f...

The prediction of opinion distribution in real-world scenarios represents a major scientific challenge for current social networks analysis. In terms of electoral forecasting, we find several prediction solutions that try to combine statistics with economic indices, and machine learning, like multilevel regression and post-stratification (MRP). Nev...

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are buil...

This is the Call for Papers and Flyer of a new journal called Journal of Smart Environments and Green Computing.

This paper proposes the Virtual Reference Feedback Tuning (VRFT) of a combination of two control algorithms, Active Disturbance Rejection Control (ADRC) as a representative data-driven (or model-free) control algorithm and fuzzy control, in order to exploit the advantages of data-driven control and fuzzy control. The combination of Active Disturban...

This article proposes an approach based on experiments to teach optimization technique (OT) courses in the Systems Engineering curricula at undergraduate level. Artificial intelligence techniques in terms of nature-inspired optimization algorithms and neural networks are inserted in the lecture and laboratory parts of the syllabus. The experiments...

The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is...