Predictive Control Based on Recurrent Neural Network and Application to Plastic Injection Molding Processes

This paper presents a predictive control based on recurrent neural network (RNN) for a class of nonlinear systems and investigates its application to temperature control of plastic injection molding processes. The RNN is used as a model identifier for approximating the nonlinear discrete-time systems and the multivariable predictive control based on the RNN is derived from a generalized predictive performance criterion. The adaptive learning rates of the RNN model and the controller are investigated via the discrete Lyapunov stability theorem, which are respectively used to guarantee the convergences of both the RNN model and the predictive controller. Finally, numerical simulations and experimental results are provided to demonstrate the effectiveness of the proposed control strategy under setpoint changes and bounded disturbances.