Genetic Approach for Neural Scheduling of Multiobjective Fuzzy PI Controllers

Author

Serra, Ginalber ; Bottura, Celso

Author_Institution

Sch. of Electr. & Comput. Eng., State Univ. of Campinas

fYear

2006

fDate

Sept. 2006

Firstpage

274

Lastpage

279

Abstract

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

Keywords

PI control; adaptive control; backpropagation; control system synthesis; discrete systems; fuzzy control; gain control; genetic algorithms; minimisation; neurocontrollers; nonlinear control systems; scheduling; backpropagation; fuzzy PI discrete controller; genetic neural scheduling; intelligent gain scheduling adaptive control; multiobjective fuzzy PI controllers; multiobjective genetic algorithm; nonlinear plants; optimal design; output response smoothing; overshoot minimization; parameter tuning; settling time minimization; Adaptive control; Adaptive scheduling; Algorithm design and analysis; Backpropagation algorithms; Fuzzy control; Fuzzy sets; Genetic algorithms; Minimization methods; Optimal control; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolving Fuzzy Systems, 2006 International Symposium on

Conference_Location

Ambleside

Print_ISBN

0-7803-9719-3

Electronic_ISBN

0-7803-9719-3

Type

conf

DOI

10.1109/ISEFS.2006.251147

Filename

4016711