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Self-Tuning Robust Fuzzy Controller Design Based on Multiobjective Particle Swarm Optimization Adaptation Mechanism

[+] Author and Article Information
Edson B.M. Costa

Federal Institute of Education, Science and Technology Department of Electroelectronics, Laboratory of Computational Intelligence Applied to Technology Av. Getúlio Vargas, 04, Monte Castelo, CEP: 65030-005, São Luís - MA, Brazil
edson.costa@ifma.edu.br

Ginalber L.O. Serra

Federal Institute of Education, Science and Technology Department of Electroelectronics, Laboratory of Computational Intelligence Applied to Technology Av. Getúlio Vargas, 04, Monte Castelo, CEP: 65030-005, São Luís - MA, Brazil
ginalber@ifma.edu.br

1Corresponding author.

ASME doi:10.1115/1.4035758 History: Received January 25, 2016; Revised January 09, 2017

Abstract

In this paper, an adaptive fuzzy controller design methodology via Multi-Objective Particle Swarm Optimization (MOPSO) based on robust stability criterion, is proposed. The plant to be controlled is modeled from its input-output experimental data considering a Takagi-Sugeno (TS) fuzzy NARX model, by using the fuzzy C-Means clustering algorithm (antecedent parameters estimation) and Weighted Recursive Least Squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital PID controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time varying delay is presented to illustrate the efficiency and applicability of the proposed methodology.

Copyright (c) 2017 by ASME
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