Authors: Ahmed Mnasser; Faouzi Bouani
Addresses: Laboratoire d'Analyse, de Conception et de Commande des Systèmes, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, LR11ES20, Tunis, Tunisie ' Laboratoire d'Analyse, de Conception et de Commande des Systèmes, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, LR11ES20, Tunis, Tunisie
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.
Keywords: minimax optimisation; neural networks; robust predictive control; stability analysis.
International Journal of Intelligent Engineering Informatics, 2018 Vol.6 No.5, pp.448 - 467
Received: 18 Nov 2017
Accepted: 14 Dec 2017
Published online: 24 Aug 2018 *