Neural network based constrained predictive control using stochastic approximation algorithm

This paper presents a constrained predictive control strategy using artificial neural networks (ANN). In this control scheme two ANN are used. The recurrent ANN is used as a multi-step ahead predictor. The control action is provided by the multilayer feedforward ANN. The weights of this ANN are estimated at each control step using a stochastic approximation (SA) algorithm by minimizing a quadratic control objective which is based on a series of the future predictions and future control actions, and by preventing violations of process constraints. Simulation results demonstrate the usefulness and the robustness of this predictive control algorithm.