Authors: Kais Bouzrara; Abdelkader Mbarek; Tarek Garna
Addresses: Laboratory of Automatic, Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia ' Laboratory of Automatic, Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia ' Laboratory of Automatic, Signal and Image Processing, Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, 4003, Sousse, Tunisia
Abstract: This paper proposes a new approach for synthesising a predictive control for non-linear uncertain process based on a proposed reduced complexity discrete-time Volterra model known as GOBF-Volterra model. This model, provided by expanding each Volterra kernel on independent generalised orthonormal basis functions (GOBF), is efficient for the synthesis of non-linear model-based predictive control (NMBPC) which copes with physical constraints and geometrical constraints due to parameter uncertainties. A quadratic criterion is optimised and a new optimisation algorithm, formulated as a quadratic programming (QP) under linear and non-linear constraints, is proposed. Simulation results on a chemical reactor are presented to illustrate the performance of the proposed NMBPC strategy for uncertain process. This reveals that the stability performance of the resulting closed-loop system depends on the choice of the tuning parameters.
Keywords: nonlinear modelling; GOBF-Volterra model; uncertain dynamic systems; min-max optimisation; model-based predictive control; uncertainty; process modelling; simulation; tuning parameters.
International Journal of Modelling, Identification and Control, 2013 Vol.19 No.4, pp.307 - 322
Published online: 28 Jul 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article