Radu-Codru David

Bio: Radu-Codru David is an academic researcher from Politehnica University of Timișoara. The author has contributed to research in topics: Optimization problem & Sensitivity (control systems). The author has an hindex of 3, co-authored 3 publications receiving 277 citations.

Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers

Abstract: This paper proposes a novel Adaptive Charged System Search (ACSS) algorithm for the optimal tuning of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). The five stages of this algorithm, namely the engagement, exploration, explanation, elaboration and evaluation, involve the adaptation of the acceleration, velocity, and separation distance parameters to the iteration index, and the substitution of the worst charged particles' fitness function values and positions with the best performing particle data. The ACSS algorithm solves the optimization problems aiming to minimize the objective functions expressed as the sum of absolute control error plus squared output sensitivity function, resulting in optimal fuzzy control systems with reduced parametric sensitivity. The ACSS-based tuning of T-S PI-FCs is applied to second-order servo systems with an integral component. The ACSS algorithm is validated by an experimental case study dealing with the optimal tuning of a T-S PI-FC for the position control of a nonlinear servo system.

Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems

Abstract: This paper suggests the optimal tuning of low-cost fuzzy controllers dedicated to a class of servo systems by means of three new evolutionary optimization algorithms: Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO) algorithm and Simulated Annealing (SA) algorithm. The processes in these servo systems are characterized by second-order models with an integral component and variable parameters; therefore the objective functions in the optimization problems include the output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The servo systems are controlled by Takagi-Sugeno proportional-integral-fuzzy controllers (T-S PI-FCs) that consist of two inputs, triangular input membership functions, nine rules in the rule base, the SUM and PROD operators in the inference engine, and the weighted average method in the defuzzification module. The T-S PI-FCs are implemented as low-cost fuzzy controllers because of their simple structure and of the only three tuning parameters because of mapping the parameters of the linear proportional-integral (PI) controllers onto the parameters of the fuzzy ones in terms of the modal equivalence principle and of the Extended Symmetrical Optimum method. The optimization problems are solved by GSA, PSO and SA resulting in fuzzy controllers with a reduced parametric sensitivity. The comparison of the three evolutionary algorithms is carried out in the framework of a case study focused on the optimal tuning of T-S PI-FCs meant for the position control system of a servo system laboratory equipment. Reduced process gain sensitivity is ensured.

Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity

Abstract: This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.

Gravitational Search Algorithm

Abstract: In this chapter, we present a gravitational search algorithm (GSA) which is based on the low of gravity. We first describe the general information of the science of gravity and the definition of mass in Sect. 22.1, respectively. Then, the fundamentals and performance of GSA are introduced in Sect. 22.2. Finally, Sect. 22.3 summarises in this chapter.

A comprehensive survey on gravitational search algorithm

Abstract: Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonian gravity in physics. Till now, many variants of GSA have been introduced, most of them are motivated by gravity-related theories such as relativity and astronomy. On the one hand, to solve different kinds of optimization problems, modified versions of GSA have been presented such as continuous (real), binary, discrete, multimodal, constraint, single-objective, and multi-objective GSA. On the other hand, to tackle the difficulties in real-world problems, the efficiency of GSA has been improved using specialized operators, hybridization, local search, and designing the self-adaptive algorithms. Researchers have utilized GSA to solve various engineering optimization problems in diverse fields of applications ranging from electrical engineering to bioinformatics. Here, we discussed a comprehensive investigation of GSA and a brief review of GSA developments in solving different engineering problems to build up a global picture and to open the mind to explore possible applications. We also made a number of suggestions that can be undertaken to help move the area forward.