Observed-Mode-Dependent State Estimation of Hidden Semi-Markov Jump Linear Systems

Cai, Bo; Zhang, Lixian; Shi, Yang

doi:10.1109/tac.2019.2919114

Cited by 95 publications

(36 citation statements)

References 15 publications

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“…Remark 7. In the derivation of V(t), U and Λ are introduced based on the sector-bounded condition (8). In the proof of Theorem 1, the model transformation 41 is not involved.…”

Section: Resultsmentioning

confidence: 99%

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Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Tong

Chen

et al. 2019

Optim Control Appl Methods

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Summary The asynchronous control for the unified model of chaotic systems with Markovian jumping parameters is considered via the Takagi‐Sugeno (T‐S) fuzzy approach in this paper. According to jumping modes for chaotic systems and modes for the asynchronous controller, a double‐mode process is generated. By introducing the hidden Markov model (HMM), the asynchronization phenomenon is represented between the controller and chaotic systems. By linear matrix inequalities (LMIs), some conditions are received to guarantee the stochastic stability and the strict dissipativity for chaotic systems. Based on the stochastic stability theory and the dissipativity theory, the asynchronous controller is discussed. The proposed method can be applied to the mode‐independent controller and the synchronous controller. Finally, the effectiveness of the proposed controller is illustrated by two examples.

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“…Remark 7. In the derivation of V(t), U and Λ are introduced based on the sector-bounded condition (8). In the proof of Theorem 1, the model transformation 41 is not involved.…”

Section: Resultsmentioning

confidence: 99%

“…The control for T‐S fuzzy Markovian jump systems was analyzed and researched . Besides, to improve the modeling ability of traditional Markovian jump systems, some questions about semi‐Markovian jump systems were discussed . These above systems have made great progress on the basis of traditional Markovian jump systems.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Tong

Chen

et al. 2019

Optim Control Appl Methods

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“…Tian et al 26 studied a class of linear semi‐MJSs for the dynamic output‐feedback control problem in discrete‐time case. Cai et al 27 focused on the state estimation for a class of hidden semi‐MJSs governed by a two‐layer stochastic process. Zhang et al 28 introduced the stability analysis and controller design for a class of nonhomogeneous hidden semi‐MJSs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics

Fang

Zhu

Stojanović

et al. 2021

Intl J Robust & Nonlinear

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An online adaptive optimal control problem for a class of nonlinear Markov jump systems (MJSs) is studied. It is worth noting that the dynamic information of MJSs is partially unknown. Applying the neural network linear differential inclusion techniques, the nonlinear terms in MJSs are approximately converted to linear forms. By using subsystem transformation schemes, we can transfer the nonlinear MJSs to N new coupled linear subsystems. Then a new online policy iteration algorithm is put forward to obtain the adaptive optimal controller. Some theorems are given afterward to ensure the convergence of the new algorithm. At last, a simulation example is provided to verify the applicability of the algorithm.

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“…Over the past few decades, linear parameter-varying (LPV) control systems have been widely researched and the results applied to several practical applications. [1][2][3][4][5][6][7][8][9] The main reason is due to the ability to represent nonlinear systems in an LPV framework. 10 As a result, relevant conditions akin to those found for linear systems in general may be derived to assess the stability and performance of this system class.…”

Section: Introductionmentioning

confidence: 99%

Discrete‐time state‐feedback control of polytopic LPV systems

Pereira

Oliveira

Kienitz

2021

Optim Control Appl Methods

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This paper focuses on a new ℋ 2 state-feedback control synthesis for discrete-time polytopic linear parameter-varying (LPV) systems. The synthesis conditions have been derived using an approach less conservative than those currently found in the literature, which allows to deal with polytopic LPV systems subject to time-varying parameters in all matrices of its state-space representation. Based on parameter-dependent Lyapunov functions, a set of linear matrix inequalities-based conditions is provided to obtain a discrete-time ℋ 2 state-feedback controller. Another significant advantage of the proposed conditions is the ability to include the poly-quadratic condition as a particular case, presenting a solution to this so far unresolved problem. In addition, using the same concept, an improved condition for quadratic ℋ 2 control synthesis is given. Finally, the effectiveness of the proposed state-feedback control design method is assessed and compared to that of similar approaches by means of numerical examples.

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Observed-Mode-Dependent State Estimation of Hidden Semi-Markov Jump Linear Systems

Cited by 95 publications

References 15 publications

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Asynchronous control of T‐S fuzzy chaotic systems via a unified model using the hidden Markov model subject to strict dissipativity

Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics

Discrete‐time state‐feedback control of polytopic LPV systems

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