CONSTRAINED LONG-RANGE PREDICTIVE CONTROL-BASED ON ARTIFICIAL NEURAL NETWORKS

A long-range predictive control strategy using artificial neural netwo rks (ANNs) is represented. Both unconstrained and constrained control problems are considered. In this control scheme a recurrent ANN and a multilayer feedforward ANN are used. The recurrent ANN is used as a mu lti-step ahead predictor. For training this network the backpropagatio n through the time is used. The control action is provided by the mult ilayer feedforward ANN which uses the predictions of the output of the process to be controlled. The weights of this ANN are estimated at ea ch control step using a stochastic approximation (SA) algorithm by min imizing a quadratic control objective which is based on a series of th e future predictions and future control actions, and by preventing vio lations of process constraints. To demonstrate the feasibility and the performance of this control scheme, a continuous biochemical reactor and a fixed bed tubular chemical reactor are chosen as realistic nonli near case studies. Simulation results demonstrate the usefulness and t he robustness of this predictive control algorithm.