Particle Swarm Optimization Algorithm based Nonlinear Model Predictive Control
نویسندگان
چکیده
-A novel approach for the implementation of Nonlinear Model Predictive Control (NMPC) using Particle Swarm Optimization (PSO) technique is proposed. Two different approaches are made in the PSO algorithms, Random PSO (RPSO) and knowledge based PSO (KPSO) for the determination of optimum controller gain in MPC structure In order to test the performance of the proposed PSO based MPC system a nonlinear Wiener model system is considered. Performances of the different algorithms are compared with respect to computation time and integral square error (ISE). Simulation results show that for the similar system performance, RPSO based MPC system takes lesser computation time than Genetic Algorithm (GA) based MPC system. Also, the KPSO based MPC system shows better performance than RPSO based MPC system. An experiment is conducted to determine the optimal length of predictive and control horizons. As the computation time required is significantly less, the proposed PSO based model predictive controllers can be used for real time control of nonlinear
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