Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information
|dc.identifier.citation||Shi, 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.publisher||Springer New York LLC|
|dc.title||Gain-Scheduled Worst-Case Control on Nonlinear Stochastic Systems Subject to Actuator Saturation and Unknown Information|
|dcterms.source.title||Journal of Optimization Theory and Applications|
|curtin.department||Department of Mathematics and Statistics|
|curtin.accessStatus||Fulltext not available|
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