H∞ Gain-Scheduled Control for LPV Stochastic Systems

A robust control problem for discrete-time uncertain stochastic systems is discussed via gain-scheduled control scheme subject to H∞ attenuation performance. Applying Linear Parameter Varying (LPV) modeling approach and stochastic difference equation, the uncertain stochastic systems can be describe...

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Main Authors: Cheung-Chieh Ku, Guan-Wei Chen
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/854957
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spelling doaj-68a2a0c43e6a4048b545f5c3ff37d4072020-11-25T00:30:58ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/854957854957H∞ Gain-Scheduled Control for LPV Stochastic SystemsCheung-Chieh Ku0Guan-Wei Chen1Department of Marine Engineering, National Taiwan Ocean University, Keelung 202, TaiwanDepartment of Marine Engineering, National Taiwan Ocean University, Keelung 202, TaiwanA robust control problem for discrete-time uncertain stochastic systems is discussed via gain-scheduled control scheme subject to H∞ attenuation performance. Applying Linear Parameter Varying (LPV) modeling approach and stochastic difference equation, the uncertain stochastic systems can be described by combining time-varying weighting function and linear systems with multiplicative noise terms. Due to the consideration of stochastic behavior, the stability in the sense of mean square is applied for the system. Furthermore, two kinds of Lyapunov functions are employed to derive their corresponding sufficient conditions to solve the stabilization problems of this paper. In order to use convex optimization algorithm, the derived conditions are converted into Linear Matrix Inequality (LMI) form. Via solving those conditions, the gain-scheduled controller can be established such that the robust asymptotical stability and H∞ performance of the disturbed uncertain stochastic system can be achieved in the sense of mean square. Finally, two numerical examples are applied to demonstrate the effectiveness and applicability of the proposed design method.http://dx.doi.org/10.1155/2015/854957
collection DOAJ
language English
format Article
sources DOAJ
author Cheung-Chieh Ku
Guan-Wei Chen
spellingShingle Cheung-Chieh Ku
Guan-Wei Chen
H∞ Gain-Scheduled Control for LPV Stochastic Systems
Mathematical Problems in Engineering
author_facet Cheung-Chieh Ku
Guan-Wei Chen
author_sort Cheung-Chieh Ku
title H∞ Gain-Scheduled Control for LPV Stochastic Systems
title_short H∞ Gain-Scheduled Control for LPV Stochastic Systems
title_full H∞ Gain-Scheduled Control for LPV Stochastic Systems
title_fullStr H∞ Gain-Scheduled Control for LPV Stochastic Systems
title_full_unstemmed H∞ Gain-Scheduled Control for LPV Stochastic Systems
title_sort h∞ gain-scheduled control for lpv stochastic systems
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description A robust control problem for discrete-time uncertain stochastic systems is discussed via gain-scheduled control scheme subject to H∞ attenuation performance. Applying Linear Parameter Varying (LPV) modeling approach and stochastic difference equation, the uncertain stochastic systems can be described by combining time-varying weighting function and linear systems with multiplicative noise terms. Due to the consideration of stochastic behavior, the stability in the sense of mean square is applied for the system. Furthermore, two kinds of Lyapunov functions are employed to derive their corresponding sufficient conditions to solve the stabilization problems of this paper. In order to use convex optimization algorithm, the derived conditions are converted into Linear Matrix Inequality (LMI) form. Via solving those conditions, the gain-scheduled controller can be established such that the robust asymptotical stability and H∞ performance of the disturbed uncertain stochastic system can be achieved in the sense of mean square. Finally, two numerical examples are applied to demonstrate the effectiveness and applicability of the proposed design method.
url http://dx.doi.org/10.1155/2015/854957
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AT guanweichen hgainscheduledcontrolforlpvstochasticsystems
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