Improving robust model predictive control via dynamic output feedback

摘要：

This paper considers robust model predictive control for systems with polytopic uncertainty and bounded disturbance, where the system state is unmeasurable, and the model at the current sampling time is an exact combination of the vertices of the polytope. A parameter-dependent dynamic output feedback is used for this problem. At each sampling time, the optimization problems can be solved via LMI techniques. By specifying quadratic boundedness, the closed-loop system is guaranteed to converge to a neighborhood of the origin. The primary contribution is the separation of the step for handling estimation error constraint in another always feasible optimization problem, being solved after the main optimization is performed. Thus, the recursive feasibility of the main optimization can be retrieved in a better manner. A numerical example is given to illustrate the effectiveness of the controller.

关键词： Dynamic output feedback Model predictive control Uncertain systems Recursive feasibility

作者: Baocang Ding Lihua Xie Fangzheng Xue

作者单位: College of Automation, Chongqing University, Chongqing, 400044, China School of Electronic and Electrical Engineering, BLK S2, Nanyang Technological University, 639798, S

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2116-2121

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）