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dc.contributor.authorShi, P.
dc.contributor.authorYin, YanYan
dc.contributor.authorLiu, F.
dc.identifier.citationShi, P. and Yin, Y. and Liu, F. 2013. Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information. Journal of Optimization Theory and Applications. 156 (3): pp. 844-858.

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.

dc.publisherSpringer New York LLC
dc.titleGain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
dc.typeJournal Article
dcterms.source.titleJournal of Optimization Theory and Applications
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available

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