2012
DOI: 10.1002/acs.2275
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Adaptive control of stochastic nonlinear systems with Markovian switching

Abstract: SUMMARYThis paper is concerned with the problem of adaptive control for a class of stochastic nonlinear systems with Markovian switching, where the upper bounds of nonlinearities of stochastic Markovian jump systems are assumed to be unknown. Firstly, an adaptation law is developed to estimate these unknown parameters. Then, a class of adaptive state feedback controller is proposed such that not only the estimated errors are bounded almost surely but also, the states of the resulting closed‐loop system are asy… Show more

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Cited by 22 publications
(7 citation statements)
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“…Definition 2 (Wang & Zhang, 2012). The solution of system (8) with u(t) ≡ 0 is said to be asymptotically stable almost surely for d(t) ≡ 0 if, for all z 20 = z 2 (t 0 ) ∈ R n , and r 0 = r(t 0 ) ∈ S, the following holds…”
Section: Design Of Virtual Control Lawmentioning
confidence: 99%
“…Definition 2 (Wang & Zhang, 2012). The solution of system (8) with u(t) ≡ 0 is said to be asymptotically stable almost surely for d(t) ≡ 0 if, for all z 20 = z 2 (t 0 ) ∈ R n , and r 0 = r(t 0 ) ∈ S, the following holds…”
Section: Design Of Virtual Control Lawmentioning
confidence: 99%
“…Afterwards, this idea was widely applied to several classes of stochastic nonlinear systems. 1,[7][8][9] In addition, the output-feedback control scheme becomes an important approach when the system states are not measurable. Compared with the state-feedback control, the output-feedback control is more difficult and challenging.…”
Section: Introductionmentioning
confidence: 99%
“…Markovian jump systems (MJSs) [1][2][3][4][5][6][7][8][9] are regarded as a special class of hybrid systems with finite operation modes. Their structures are subject to random abrupt changes, which may result from abrupt phenomena such as random component failures or repairs, changes in subsystem interconnections, and sudden environmental changes and modification of the operating point of a linearized model of a nonlinear system.…”
Section: Introductionmentioning
confidence: 99%