Abstract:

An optimal selection approach of support vector regression (SVR) parameters is proposed based on the multi-Agent particle swarm optimization (MAPSO) algorithm. A multi-step predictive control model based on the SVR to predict nonlinear systems is established; and the optimal parameters of which is searched by MAPSO. With the objective function of rolling optimization, analytical solutions of multi-step predictive control laws are obtained by the predictive control mechanism. Comparing with the model predictive controllers based on SVR optimized by the particle swarm optimization algorithm (PSO-SVR), SVR optimized genetic algorithm (GA-SVR), and RBF neural network algorithm optimized genetic algorithm (GA-RBF), the simulation results show that the proposed method has better prediction results than others and is effective for nonlinear systems.