Fuzzy controller training using particle swarm optimization for nonlinear system control.
This paper proposes and describes an effective utilization of particle swarm optimization (PSO) to train a Takagi-Sugeno (TS)-type fuzzy controller. Performance evaluation of the proposed fuzzy training method using the obtained simulation results is provided with two samples of highly nonlinear systems: a continuous stirred tank reactor (CSTR) and a Van der Pol (VDP) oscillator. The superiority of the proposed learning technique is that there is no need for a partial derivative with respect to the parameter for learning. This fuzzy learning technique is suitable for real-time implementation, especially if the system model is unknown and a supervised training cannot be run. In this study, all parameters of the controller are optimized with PSO in order to prove that a fuzzy controller trained by PSO exhibits a good control performance.
Karakuzu
Cihan
C
Department of Electronics & Telecommunications Engineering, Engineering Faculty, Kocaeli University, 41040, Izmit-Kocaeli, Turkey.
D000465:Algorithms; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D005246:Feedback; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D013598:Systems Theory