Improving control performance of robust MPC by iterative optimization
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.
Model predictive control Uncertain systems Iterative optimization Slack matriz Feedback gain
Baocang Ding Fangzheng Xue
College of Automation, Chongqing University, Chongqing, 400044, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2807-2812
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）