Integration of PSO and GA for optimum design of fuzzy PID controllers in a pendubot system |
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| Authors: | Yu-Yi Fu Chia-Ju Wu Ting-Li Chien Chia-Nan Ko |
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| Affiliation: | (1) Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan;(2) Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan;(3) Department of Electronic Engineering, Wu-Feng Institute of Technology, Chiayi, Taiwan;(4) Department of Automation Engineering, Nan-Kai University of Technology, 568, Chung-Cheng Rd. Tasotun, Nantou, 542, Taiwan |
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| Abstract: | In this paper, a novel auto-tuning method is proposed to design fuzzy PID controllers for asymptotical stabilization of a
pendubot system. In the proposed method, a fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables
are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are
adaptive and the fuzzy PID controller has more flexibility and capability than the conventional ones with fixed gains. To
tune the fuzzy PID controller simultaneously, an evolutionary learning algorithm integrating particle swarm optimization (PSO)
and genetic algorithm (GA) methods is proposed. The simulation results illustrate that the proposed method is indeed more
efficient in improving the asymptotical stability of the pendubot system.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 |
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| Keywords: | Particle swarm optimization Genetic algorithm Fuzzy PID controllers Pendubot system |
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