Hidden Markov model-based asynchronous quantized sampled-data control for fuzzy nonlinear Markov jump systems

Wu, Tao; Xiong, Lianglin; Cao, Jinde; Park, Ju H.

doi:10.1016/j.fss.2021.08.016

Cited by 22 publications

(7 citation statements)

References 49 publications

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“…Then, the LMIs (22) hold by using the change of variable Ψ i = µ i Σ i . Moreover, it is evident by the system (1) by (u(t) = 0) that in the presence of time delays it is stochastically stable, and this completes the proof.…”

Section: Resultsmentioning

confidence: 99%

“…Remark 1. Before expressing the next Theorem, since LMIs (22) are affine in respect of A i and A i , sufficient conditions for investigating the robust stochastic stability in the presence of polytopic uncertainties can be acquired by considering the proposed LMIs only in polytopic uncertainty vertices for each mode. In other words, the corresponding system subject to polytopic uncertainties has robust stochastic stability if the following LMIs have feasible solutions:…”

Section: Resultsmentioning

confidence: 99%

“…The fuzzy controllers were investigated in the Ref. [22], and the problem of stochastic stability was analyzed.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Control for Time Delay Markov Jump Systems under Polytopic Uncertainties

et al. 2022

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The Markov jump systems (MJSs) are a special case of parametric switching system. However, we know that time delay inevitably exists in many practical systems, and is known as the main source of efficiency reduction, and even instability. In this paper, the stochastic stable control design is discussed for time delay MJSs. In this regard, first, the problem of modeling of MJSs and their stability analysis using Lyapunov-Krasovsky functions is studied. Then, a state-feedback controller (SFC) is designed and its stability is proved on the basis of the Lyapunov theorem and linear matrix inequalities (LMIs), in the presence of polytopic uncertainties and time delays. Finally, by various simulations, the accuracy and efficiency of the proposed methods for robust stabilization of MJSs are demonstrated.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Automatic Control for Time Delay Markov Jump Systems under Polytopic Uncertainties

et al. 2022

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“…Post-and pre-multiplied (11) and ( 12) by diag{X j , X j , X j , X j , X j , X j , X j } and its transpose if 𝜎 m = (i, j), by diag{X i , X i , X i , X i , X i , X i , X i } and its transpose if 𝜎 m = (i, i), respectively, then (28) and ( 29) can be obtained. Similarly, post-and pre-multiplied ( 14) by X j and its transpose, then (30) can be obtained.…”

Section: Control Designmentioning

confidence: 99%

“…26,27 However, as for switched systems under event-triggered control, asynchronous switching may occur if the system mode is updated but the next triggering instant has not been generated yet. 28,29 Therefore, there are many works regarding asynchronous switching problem of switched systems under the event-triggering mechanism. 6,22,30 Reference 6 studied the finite-time asynchronous control problem for switched linear systems based on the SETM.…”

Section: Introductionmentioning

confidence: 99%

Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme

Wang,

Huang

et al. 2023

Intl J Robust & Nonlinear

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This paper investigates the asynchronous control of delayed switched neural networks via a dynamic event‐triggering mechanism (DETM) and a merging signal scheme. Firstly, an improved DETM is proposed through adding an exponential decaying term into the triggering condition, which is proved to have a larger lower bound by comparison with the static event‐triggering mechanism (SETM) and the existing DETM. Next, a merging signal is constructed by introducing a time‐varying delay to combine the system switching signal with the controller switching signal, which is convenient to deal with the synchronous and asynchronous switching in a unified framework. Then, the closed‐loop system which integrates the proposed DETM with the merging signal is formulated. Moreover, by constructing multiple time‐dependent Lyapunov functionals, sufficient conditions which ensure the asymptotical stability of the resulting closed‐loop system are derived, and a design algorithm for the feedback controller is designed consequently. Finally, the validity and superiority of the proposed results are demonstrated by numerical examples.

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Synchronization of stochastic switching complex networks with a target of nonzero inputs

Tian,

Wang

2023

Adaptive Control & Signal

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SummaryIn this article, we study the mean square synchronization problems for complex networks with a target of nonzero inputs under stochastic switching topology that is driven by an ergodic continuous‐time Markov process. First, we design an adaptive continuous controller based on relative full states that is consist of a synchronization term and an uncertainty elimination term, where the latter term can completely eliminate the effects of target's nonzero inputs and is continuous by adding an exponential decay function with positive initial values into the denominator. By developing a stochastic Lyapunov function, we show that zero error synchronization in the mean square sense can be achieved if the union of switching graphs contains a directed spanning tree with the target being the root. Second, we design an adaptive continuous controller based on relative outputs. Finally, we give two simulation examples to validate the theoretical results.

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Hidden Markov model-based asynchronous quantized sampled-data control for fuzzy nonlinear Markov jump systems

Cited by 22 publications

References 49 publications

Automatic Control for Time Delay Markov Jump Systems under Polytopic Uncertainties

Automatic Control for Time Delay Markov Jump Systems under Polytopic Uncertainties

Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme

Synchronization of stochastic switching complex networks with a target of nonzero inputs

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