Adaptive neural network-based predictive control for nonlinear dynamical systems

In the paper, we propose a predictive control scheme using a neural network-based prediction model for nonlinear processes. To identify the system dynamics, we approximate the nonlinear function with an affine function of some of its arguments and construct a special type of prediction model using three-layered feedforward neural networks. Using some available input-output data pairs of the plant, we estimate the weights of neural networks by the Gauss-Newton based Levenberg-Marquard method. To cope with load disturbances and reduce the effect of unmodelled dynamics in the control system, we implement an on-line adaptation algorithm. Comparative simulations are given to show superiority of the proposed predictive control method to the adaptive GPC algorithm for some processes.