Real-Time Neural Network Control - An IMC Approach

Abstract An internal model control (IMC) structure provides a practical framework to nonlinear control design. Current nonlinear IMC design procedures are restricted to open-loop stable systems with stable inverse. In this paper, a novel IMC design method is presented for time-discrete, nonlinear single-input single-output systems. The design method is based on a nonlinear parametric optimal control law which is realized by a perceptron network. The parameters of the network are obtained by minimizing the cost function by the recursive prediction error method (RPEM).Also, a parameter projection is employed to restrict the parameters of the controller into the stable region of the parameter space. This procedure relaxes the assumption about the stability of the model inverse. The properties of the resulting controller are studied in real-time experiments with a laboratory heating process. The results demonstrate that the proposed design method yields good performance characteristics and robust control