Genetic Approach for Neural Scheduling of Multiobjective Fuzzy PI Controllers

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