Title: Experimental implementation of MIMO model predictive controller-based second order divided difference filter for nonlinear systems
Authors: Hichem Salhi; Faouzi Bouani
Addresses: LR11ES20, Laboratoire Analyse, Conception et Commande des Systèmes, Faculté des Sciences de Tunis, Université de Tunis El Manar, Tunisia ' LR11ES20, Laboratoire Analyse, Conception et Commande des Systèmes, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunisia
Abstract: In this paper, we propose an experimental implementation of a nonlinear predictive controller based on the second order divided difference filter (DDF2) which is a derivate-free estimator. The used state estimator avoids the determination of Jacobian matrices required with the extended Kalman filter (EKF) for an easy implementation with nonlinear systems. In addition, the second order Stirling's interpolation of the estimator allows a more accurate a priori state prediction. A constrained nonlinear predictive controller-based DDF2 is designed to control a multivariable three-tank system. In order to generate the control signals, the controller solves a nonlinear objective function. To overcome the offset output error that appeared due to the inaccuracy of the system model, an internal state model correction scheme is adopted at each iteration. Practical results show the reliability of the proposed method in state estimation, setpoint tracking, and smooth control signal generation. The proposed controller outperforms the model predictive control based on linear state space model derived from the linearisation of the system model around the same setpoint trajectory.
Keywords: nonlinear model predictive control; NMPC; derivate-free estimator; offset-free output; application.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.3, pp.191 - 199
Available online: 22 Apr 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article