Particle Swarm Optimization-based Fuzzy Predictive Control Strategy

Particle Swarm Optimization (PSO) is proposed as an efficient tool for the design of fuzzy predictive control (FPC) strategies. The performance of the proposed method is evaluated in terms of accuracy and computational time. PSO is compared with other evolutionary algorithms such as simple genetic algorithms and niching genetic algorithms. Simulation results that validate the proposed FPC-PSO scheme are presented for a benchmark non-linear series.