Journal
IFAC PAPERSONLINE
Volume 51, Issue 1, Pages 329-334Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ifacol.2018.05.040
Keywords
Model Predictive Control; Adaptive Control; Input Constraints
Categories
Ask authors/readers for more resources
In this paper, an adaptive model predictive control (MPC) strategy is proposed for controlling a discrete -time linear MIMO system with parametric uncertainties and subjected to actuator constraints. Compared to previous results in literature which either solve the constrained MPC problem for stable uncertain systems or the unconstrained MPC problem for unstable uncertain systems, this result, presents a solution approach for constrained MPC problems for fully uncertain and unstable systems. An adaptive law, designed to update the estimated parameters of the plant, is combined with a constrained MPC for an estimated system. A sufficient condition is imposed on the adaptation gain to account for feasibility of the MPC optimization problem in the presence of the actuator constraint. Stability analysis of the closed loop system with the proposed adaptive MPC strategy has been shown to guarantee the ultimate boundedness of the parameter estimation errors and boundedness as well as asymptotic convergence of the tracking errors to zero. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available