Stable robust predictive controller for nonlinear systems

Abstract

Stability of robust model predictive controller for SISO nonlinear dynamical systems is established in this paper. The neural networks model with parameter uncertainties is used to approximate the process behaviour having different point functions. The control input action is obtained by solving online the minimax optimisation problem subject to the model uncertainties and the input constraints. We have also studied the stability of the closed loop system in the presence of model uncertainties by using the Lyapunov theory. A comparison study between the PID controller and the proposed robust predictive controller was performed to validate the feasibility of the use of the uncertain neural networks in control theory. A simulation example is presented in order to illustrate the efficiency of the proposed controller.