Authors:
Amira Kheriji
;
Faouzi Bouani
and
Mekki Ksouri
Affiliation:
National Engineering School of Tunis, Tunisia
Keyword(s):
Predictive control, Parametric uncertainty, State space model, Generalized geometric programming, Constrained control, Set-point tracking, Disturbance rejection.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The goal of this paper is to evaluate the closed loop performances of a new approach in constrained state space Robust Model Predictive Control (RMPC) in the presence of parametric uncertainties. The control law is obtained by the resolution of a min-max optimization problem, initially non convex, under input and input deviation constraints, using worst case strategy. The technique used is the Generalized Geometric Programming (GGP) which is a global optimization method for non convex functions constrained in a specific domain. The key idea of the proposed approach is the convexification of the optimization problem allowing to compute the optimal control law using standard optimization technique. The proposed method is efficient since it guarantees set-point tracking different from the origin and non zero disturbances rejection. The efficiency of this approach is illustrated with two examples and compared with a recent state space RMPC algorithm.