Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/82169
Type: Artigo de periódico
Title: Multiobjective evolution based fuzzy PI controller design for nonlinear systems
Author: Serra, GLO
Bottura, CP
Abstract: This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators. (c) 2005 Elsevier Ltd. All rights reserved.
Subject: neural-genetic-fuzzy systems
adaptive control
multiobjective optimization
Country: Inglaterra
Editor: Pergamon-elsevier Science Ltd
Rights: embargo
Identifier DOI: 10.1016/j.engappai.2005.08.003
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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