A New Approach for Fuzzy Predictive Adaptive Controller Design Using Particle Swarm Optimization Algorithm
نویسندگان
چکیده
This paper introduces a new approach for designing an adaptive fuzzy model predictive control (AFMPC) using the Particle Swarm Optimization (PSO) algorithm. The system to be controlled is modeled by a Takagi-Sugeno fuzzy inference system whose parameters are identified using recursive least square algorithm. These parameters are used to calculate the objective function based on predictive approach and structure of RST controller. The controller design methodology is formulated as an optimization problem solved by PSO algorithm to obtain the optimal future control. The approach was applied for controlling two non linear systems CSTR and Tank system. The results are encouraging compared with those obtained using the Proportional Integral-Particle Swarm Optimization (PI-PSO) and adaptive fuzzy model predictive control (AFMPC).
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