Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems
TL;DR: 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, Particle Swarm Optimization algorithm and Simulated Annealing algorithm.
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.
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01 Jan 2014
TL;DR: In this chapter, a gravitational search algorithm (GSA) which is based on the low of gravity is presented, and the fundamentals and performance of GSA are introduced.
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.
207 citations
TL;DR: In this article, an opposition-based gravitational search algorithm (OGSA) is applied for the solution of optimal reactive power dispatch (ORPD) of power systems, which is defined as the minimization of active power transmission losses by controlling a number of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation.
182 citations
TL;DR: This paper proposes the design of fuzzy control systems with a reduced parametric sensitivity making use of Gravitational Search Algorithms (GSAs), and suggests a GSA with improved search accuracy.
150 citations
Cites methods from "Evolutionary optimization-based tun..."
...Several optimal control applications, which employ sensitivity models, are reported in the literature [1,17,23,45,64], and some current approaches are: an application of Bellman-Zadeh’s approach to decision making in fuzzy environments for multi-criteria optimization problems is analyzed in [10]; the elimination of the steadystate control error by an augmented state feedback tracking guaranteed cost control is suggested in [62]; optimal control approaches to human arm movement control are given in [7]; a method to estimate the minimum variance bounds and the achievable ones for Iterative Learning Control-based batch control systems is investigated in [8]; the evolutionary-based optimization of fuzzy control systems resulting in proportional-integral fuzzy controllers (PI-FCs) for servo systems by means of Particle Swarm Optimization (PSO) algorithms, Simulated Annealing (SA) algorithms, Gravitational Search Algorithms and Charged System Search (CSS) algorithms is discussed in [39,41,43]....
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TL;DR: The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system and nature-inspired optimal control.
148 citations
TL;DR: In this paper, a bat-inspired algorithm based dual mode PI controller is applied to the multi-area interconnected thermal power system in order to tune the parameter PI controllers, which provides better transient as well as steady state of response.
132 citations
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
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TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
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TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
5,501 citations