A convex optimization approach to adaptive stabilization of discrete‐time LTI systems with polytopic uncertainties. (23rd November 2014)

• Record Type: Journal Article
• Authors: Lee, Dong Hwan; Joo, Young Hoon; Tak, Myung Hwan
• Is Part Of: International journal of adaptive control and signal processing. Volume 29:Number 9(2015:Sep.)
• Page Start: 1116
• Page End: 1134
• Publication Date: 2014-11-23
• Subjects: Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.2525
• Languages: English
• ISSNs: 0890-6327

Abstract

<abstract abstract-type="main" id="acs2525-abs-0001"> <title>Summary</title> <p id="acs2525-para-0001">This paper suggests a simple convex optimization approach to state‐feedback adaptive stabilization problem for a class of discrete‐time LTI systems subject to polytopic uncertainties. The proposed method relies on estimating the uncertain parameters by solving an online optimization at each time step, such as a linear or quadratic programming, and then, on tuning the control law with that information, which can be conceptually viewed as a kind of gain‐scheduling or indirect adaptive control. Specifically, an admissible domain of stabilizing state‐feedback gain matrices is designed offline by means of linear matrix inequality problems, and based on the online estimation of the uncertain parameters, the state‐feedback gain matrix is calculated over the set of stabilizing feedback gains. The proposed stabilization algorithm guarantees the asymptotic stability of the overall closed‐loop control system. An example is given to show the effectiveness of the proposed approach. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>