Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information

In this paper, we propose a method for designing continuous gain-scheduled worst-case controller for a class of stochastic nonlinear systems under actuator saturation and unknown information. The stochastic nonlinear system under study is governed by a finite-state Markov process, but with partially...

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Main Authors: Shi, P., Yin, YanYan, Liu, F.
Format: Journal Article
Published: Springer New York LLC 2013
Online Access:http://hdl.handle.net/20.500.11937/51998
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author Shi, P.
Yin, YanYan
Liu, F.
author_facet Shi, P.
Yin, YanYan
Liu, F.
author_sort Shi, P.
building Curtin Institutional Repository
collection Online Access
description In this paper, we propose a method for designing continuous gain-scheduled worst-case controller for a class of stochastic nonlinear systems under actuator saturation and unknown information. The stochastic nonlinear system under study is governed by a finite-state Markov process, but with partially known jump rate from one mode to another. Initially, a gradient linearization procedure is applied to describe such nonlinear systems by several model-based linear systems. Next, by investigating a convex hull set, the actuator saturation is transferred into several linear controllers. Moreover, worst-case controllers are established for each linear model in terms of linear matrix inequalities. Finally, a continuous gain-scheduled approach is employed to design continuous nonlinear controllers for the whole nonlinear jump system. A numerical example is given to illustrate the effectiveness of the developed techniques. © 2012 Springer Science+Business Media, LLC.
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institution Curtin University Malaysia
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publishDate 2013
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spelling curtin-20.500.11937-519982017-09-13T15:38:22Z Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information Shi, P. Yin, YanYan Liu, F. In this paper, we propose a method for designing continuous gain-scheduled worst-case controller for a class of stochastic nonlinear systems under actuator saturation and unknown information. The stochastic nonlinear system under study is governed by a finite-state Markov process, but with partially known jump rate from one mode to another. Initially, a gradient linearization procedure is applied to describe such nonlinear systems by several model-based linear systems. Next, by investigating a convex hull set, the actuator saturation is transferred into several linear controllers. Moreover, worst-case controllers are established for each linear model in terms of linear matrix inequalities. Finally, a continuous gain-scheduled approach is employed to design continuous nonlinear controllers for the whole nonlinear jump system. A numerical example is given to illustrate the effectiveness of the developed techniques. © 2012 Springer Science+Business Media, LLC. 2013 Journal Article http://hdl.handle.net/20.500.11937/51998 10.1007/s10957-012-0142-2 Springer New York LLC restricted
spellingShingle Shi, P.
Yin, YanYan
Liu, F.
Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
title Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
title_full Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
title_fullStr Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
title_full_unstemmed Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
title_short Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
title_sort gain-scheduled worst-case control on nonlinear stochastic systems subject to actuator saturation and unknown information
url http://hdl.handle.net/20.500.11937/51998