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

Peng Shi (), Yanyan Yin () and Fei Liu ()
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Peng Shi: Victoria University
Yanyan Yin: Jiangnan University
Fei Liu: Jiangnan University

Journal of Optimization Theory and Applications, 2013, vol. 156, issue 3, No 16, 844-858

Abstract: Abstract 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.

Keywords: Continuous gain scheduling; Actuator saturation; Worst-case control; Unknown information; Markov jump system; Stochastic stability; Nonlinear equations and systems; Hybrid systems (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-012-0142-2

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