Open AccessNonfragile Gain-Scheduled Control for Discrete-Time Stochastic Systems with Randomly Occurring Sensor Saturations
Author(s) -
Wangyan Li,
Guoliang Wei,
Hamid Reza Karimi,
Xiaohui Liu
Publication year - 2013
Publication title -
abstract and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.228
H-Index - 56
eISSN - 1687-0409
pISSN - 1085-3375
DOI - 10.1155/2013/629621
Subject(s) - bernoulli distribution , mathematics , control theory (sociology) , bernoulli's principle , semidefinite programming , convex optimization , mathematical optimization , probability distribution , discrete time and continuous time , controller (irrigation) , regular polygon , lyapunov function , control (management) , computer science , random variable , nonlinear system , statistics , engineering , agronomy , physics , geometry , quantum mechanics , artificial intelligence , biology , aerospace engineering
This paper is devoted to tackling the control problem for a class of discrete-time stochastic systems with randomly occurring sensor saturations. The considered sensor saturation phenomenon is assumed to occur in a random waybased on the time-varying Bernoulli distribution with measurable probability in real time. The aim of the paper is to design a nonfragile gain-scheduled controller with probability-dependent gains which can be achieved by solving a convex optimization problem via semidefinite programming method. Subsequently, a new kind of probability-dependent Lyapunov functional is proposed in order to derive the controller with less conservatism. Finally, an illustrative example will demonstrate the effectiveness of our designed procedures
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