Title :
Improving control performance of robust MPC by iterative optimization
Author :
Ding, Baocang ; Xue, Fangzheng
Author_Institution :
Coll. of Autom., Chongqing Univ., Chongqing, China
Abstract :
This paper addresses robust MPC for constrained systems with polytopic uncertainty description. Firstly, in the technique which parameterizes the infinite horizon control moves into a single state feedback law and invokes the parameter-dependent Lyapunov method for achieving closed-loop stability, the slack matrices are iteratively solved at each sampling time. Secondly, in the technique which parameterizes the infinite horizon control moves into a set of free perturbations followed by a single state feedback law, the feedback gains within the switch horizon are iteratively found at each sampling time, rather than just inherited from the previous sampling time. Numerical examples show that iterative MPC can not only improve the control performance, but also enlarge the region of attraction.
Keywords :
Lyapunov methods; closed loop systems; iterative methods; matrix algebra; optimisation; predictive control; robust control; state feedback; uncertain systems; closed-loop stability; infinite horizon control; iterative optimization; model predictive control; parameter-dependent Lyapunov method; polytopic uncertainty description; robust MPC; single state feedback law; slack matrices; uncertain systems; Automatic control; Infinite horizon; Linear matrix inequalities; Lyapunov method; Open loop systems; Robust control; Sampling methods; State feedback; Symmetric matrices; Uncertain systems; Feedback gain; Iterative optimization; Model predictive control; Slack matrix; Uncertain systems;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5194914