PSO-based model predictive control for nonlinear processes

A novel approach for the implementation of nonlinear model predictive control (MPC) is proposed using neural network and particle swarm optimization (PSO). A three-layered radial basis function neural network is used to generate multi-step predictive outputs of the controlled process. A modified PSO with simulated annealing is used at the optimization process in MPC. The proposed algorithm enhances the convergence and accuracy of the controller optimization.. Applications to a discrete time nonlinear process and a thermal power unit load system are studied. The simulation results demonstrate the effectiveness of the proposed algorithm.