NDLI: Improving robust model predictive control via dynamic output feedback

Content Provider	IEEE Xplore Digital Library
Author	Baocang Ding Lihua Xie Fangzheng Xue
Copyright Year	2009
Description	Author affiliation: School of Electronic and Electrical Engineering, BLK S2, Nanyang Technological University, 639798, Singapore (Lihua Xie) \|\| College of Automation, Chongqing University, 400044, China (Baocang Ding; Fangzheng Xue)
Abstract	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.
Starting Page	2116
Ending Page	2121
File Size	169631
Page Count	6
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5192398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dynamic output feedback Estimation error Uncertain systems Uncertainty Symmetric matrices Predictive models Robust control Constraint optimization Model predictive control Recursive feasibility Sampling methods Robustness Output feedback Predictive control
Content Type	Text
Resource Type	Article

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