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• W1570208132 abstract "In this paper, the gain-scheduled control problem is addressed by using probability-dependent Lyapunov functions for a class of discrete-time stochastic delayed systems with randomly occurring sector nonlinearities. The sector nonlinearities are assumed to occur according to a time-varying Bernoulli distribution with measurable probability in real time. The multiplicative noises are given by means of a scalar Gaussian white noise sequence with known variances. The aim of the addressed gain-scheduled control problem is to design a controller with scheduled gains such that, for the admissible randomly occurring nonlinearities, time delays and external noise disturbances, the closed-loop system is exponentially mean-square stable. Note that the designed gain-scheduled controller is based on the measured time-varying probability and is therefore less conservative than the conventional controller with constant gains. It is shown that the time-varying controller gains can be derived in terms of the measurable probability by solving a convex optimization problem via the semi-definite programme method. A simulation example is exploited to illustrate the effectiveness of the proposed design procedures. Copyright © 2012 John Wiley & Sons, Ltd." @default.
• W1570208132 created "2016-06-24" @default.
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• W1570208132 date "2012-03-06" @default.
• W1570208132 modified "2023-10-14" @default.
• W1570208132 title "Probability-dependent gain-scheduled control for discrete stochastic delayed systems with randomly occurring nonlinearities" @default.
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• W1570208132 doi "https://doi.org/10.1002/rnc.2788" @default.
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