Smith predictive control based on elman network compensatory model

Smith predictive control based on the Elman network compensatory model is studied. A mutually compensatory modeling method is employed, in which the mechanism model simulates the main performance of the controlled process. As the mechanism model inevitably involves modeling error more or less, an Elman network can be used to model the modeling error of the mechanism model, and to compensate it. The simulation is performed following these ideas. The results prove that the Smith predictive control algorithm based on Elman network compensatory model takes good advantage of the nonlinear modeling capability of the neural network, and that the harm from the time delay to the performance and stability of the system can be counteracted completely if only the time delay is precisely known. Accordingly, with the help of the Elman network compensatory model, Smith predictor can be advanced to control the system whose mathematical model is difficult to determine precisely.