Sampled‐data reliable control for T–S fuzzy semi‐Markovian jump system and its application to single‐link robot arm model

Cheng, Jun; Wang, Bo; Park, Ju H.; Kang, Wei

doi:10.1049/iet-cta.2016.1462

Cited by 55 publications

(28 citation statements)

References 36 publications

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“…Moreover, to show the superiority of this developed control method, we compare our method with the approach in the work of Cheng et al The result is shown in Figure , and we can find that our method can perfectly overcome both actuator failures while the method in the work of Cheng et al is slightly inferior. In addition, the gains of controllers that are given by our method are smaller than the ones in the aforementioned work . Thus, the controllers calculated by our method will consume less energy, achieve better control effects, extend the life of the controllers, and so on.…”

Section: Numerical Examplementioning

confidence: 92%

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Mixed passive / H_∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

et al. 2018

Intl J Robust & Nonlinear

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Summary In this paper, mixed passive / H∞ hybrid controllers are designed for a delayed Markovian jump system with actuator constraints and alarm signal. The actuator constraints include the unknown actuator bias failure and the actuator partial failure, and the controllers are switched by using alarm signals. First, a sufficient condition for mixed passive / H∞ performance of the closed‐loop system is constructed, and robust controllers and fault‐tolerant controllers are designed, respectively. Then, mixed passive / H∞ observers are presented to ensure that the actuator bias failure is well estimated. Next, an alarm system is proposed by designing multiple thresholds to avoid false alarm, and this system is used to invoke suitable controllers and determine whether to remove the estimated value of actuator bias failure. Finally, two numerical examples are given to illustrate the effectiveness of the method.

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Section: Numerical Examplementioning

confidence: 92%

“…In addition, the gains of controllers that are given by our method are smaller than the ones in the aforementioned work. 26 Thus, the controllers calculated by our method will consume less energy, achieve better control effects, extend the life of the controllers, and so on. Example 2.…”

Section: Example 1 Consider the Following Systemsmentioning

confidence: 93%

Mixed passive / H_∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

et al. 2018

Intl J Robust & Nonlinear

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“…The

L_{\infty}

control for positive delay systems with semi‐Markov process was stuided in Reference . The Takagi‐Sugeno model approach was introduced to fuzzy control of nonlinear single‐link robot arm model with semi‐Markov switching parameters in References and , and for more details, please refer to References and and reference therein.…”

Section: Introductionmentioning

confidence: 99%

Further criterion for stochastic stability analysis of semi‐Markovian jump linear systems

Jiang

Karimi

2020

Intl J Robust & Nonlinear

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This article is devoted to provide further criterion for stochastic stability analysis of semi-Markovian jump linear systems (S-MJLSs), in which more generic transition rates (TRs) will be studied. As is known, the time-varying TR is one of the key issues to be considered in the analysis of S-MJLS. Therefore, this article is to investigate general cases for the TRs that covered almost all types, especially for the type that the jumping information from one mode to another is fully unknown, which is merely investigated before. By virtue of stochastic functional theory, sufficient conditions are developed to check stochastic stability of the underlying systems via linear matrix inequalities formulation combined with a maximum optimization algorithm. Finally, a numerical example is given to verify the validity and effectiveness of the obtained results. K E Y W O R D Slinear matrix inequalities, semi-Markovian jump systems, stochastic stability, uncertain transition rates INTRODUCTIONThe semi-Markovian jump linear systems (S-MJLSs) 1 have received considerable attention in the recent control theory literature due to their better reflection of physical systems than Markovian jump linear systems (MJLSs). Traditionally, the MJLSs are applied to model physical systems with abrupt structure changes 2-6 with great efficiency, but with high demand for accuracy of modeling physical systems nowadays, the MJLS approach apparently shows its limitations, which mainly derives from its sojourn-time h follows a memory-less exponential distribution. In a continuous-time S-MJLS, the sojourn-time h follows more general continuous probability distributions, such as the Weibull distribution, rather than confined to the exponential distribution in MJLSs. The consequence is that the transition rates (TRs) in S-MJLSs are relaxed from constant to sojourn-time dependent time-varying parameters ij (h). Due to the general property of S-MJLSs, the problems of stochastic stability analysis, stabilization, and filtering of S-MJLSs are particularly becoming hot topics in the literature. For example, the robust stochastic stability and the control design problems for S-MJLSs were studied in References 7-9. The concern on the stochastic stability and stabilization for a class of discrete-time S-MJLSs was dealt with in References 10 and 11. The observer-based sliding mode approach was proposed for stabilization of S-MJLSs in References 12-14. The ℒ ∞ control for positive delay systems with semi-Markov process was stuided in Reference 15. The Takagi-Sugeno model approach was introduced to fuzzy control of nonlinear single-link robot arm model with semi-Markov switching parameters in References 16 and 17, and for more details, please refer to References 18 and 19 and reference therein. Int J Robust Nonlinear Control. 2020;30:2689-2700. wileyonlinelibrary.com/journal/rnc

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“…As pointed out in [28], random sampling schemes are preferable to deterministic sampling schemes. [29,30,31,32,33,34,35,36] considered the consensus problem of MASs during random sampling of continuous systems for the purpose of reducing energy consumption or in the case of network attacks during data transmission, and the sampling probabilities are preciously known. However, it is worth mentioning that the above results were obtained based on the accurate statistical information of DoS attack process, which has great limitations in practical applications as the attack behavior is usually hard to capture.…”

Section: Introductionmentioning

confidence: 99%

Consensus of heterogeneous multi-agent systems with uncertain DoS attack: Application to mobile stage vehicles

Yu¹,

Ni²,

Dong³

et al. 2020

Kybernetika

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Sampled‐data reliable control for T–S fuzzy semi‐Markovian jump system and its application to single‐link robot arm model

Cited by 55 publications

References 36 publications

Mixed passive / H_∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

Mixed passive / H_∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

Further criterion for stochastic stability analysis of semi‐Markovian jump linear systems

Consensus of heterogeneous multi-agent systems with uncertain DoS attack: Application to mobile stage vehicles

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Sampled‐data reliable control for T–S fuzzy semi‐Markovian jump system and its application to single‐link robot arm model

Cited by 55 publications

References 36 publications

Mixed passive / H∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

Mixed passive / H∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

Further criterion for stochastic stability analysis of semi‐Markovian jump linear systems

Consensus of heterogeneous multi-agent systems with uncertain DoS attack: Application to mobile stage vehicles

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Mixed passive / H_∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm

Mixed passive / H_∞ hybrid control for delayed Markovian jump system with actuator constraints and fault alarm