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Multiobjective evolution based fuzzy PI controller design for nonlinear systems
Affiliation:1. The State Key Laboratory for Manufacturing Systems Engineering, and Systems Engineering Institute, Xi''an Jiaotong University, Xi''an 710049, China;2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, NJ 07102, USA;3. School of Sciences, Xi''an University of Technology, Xi''an 710054, China
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.
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