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Genetic Approach for Neural Scheduling of Multiobjective Fuzzy PI Controllers

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2 Author(s)
Ginalber Serra ; Control and Intelligent Systems Laboratory, School of Electrical and Computer Engineering, State University of Campinas, 400, Cid. Univ. Zeferino Vaz, 13083-970, Campinas-SP, Brazil. ginalber@dmcsi.fee.unicamp.br ; Celso Bottura

This paper presents an intelligent gain scheduling adaptive control approach for nonlinear plants. A fuzzy PI discrete controller is optimally designed by using a multiobjective 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 for adaptive speed control of a DC servomotor used as actuator of robotic manipulators

Published in:

2006 International Symposium on Evolving Fuzzy Systems

Date of Conference:

Sept. 2006