Stabilization of hidden semi-Markov jump systems: Emission probability approach

Zhang, Lixian; Cai, Bo; Shi, Yang

doi:10.1016/j.automatica.2018.11.027

Cited by 94 publications

(26 citation statements)

References 16 publications

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“…(S1) Obtain the values of 0-th observer (x [0] (t),f [0] (t)) by performing (4), then take n = 1. (S2) Getx [n] (t) andf [n] (t) from (6), and calculate…”

Section: Remark 6 Guaranteed By the Stochastic Stability Establishedmentioning

confidence: 99%

“…Some typical applications are commonly founded in power systems and biochemical processes [2]. As a consequence, theoretical studies on Markov jump systems (MJSs) are fruitful, such as stability and stabilization [3]- [6], H 2 and H ∞ [7]- [11], sliding mode control [12], [13], quantization control [14], [15], asynchronous control [16], [17], reliable control [18], just mentioned a few.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Tracking Control for Markov Jump Systems With Mismatched Faults by Iterative Proportional–Integral Observers

Shen¹,

Ma²,

Park

et al. 2022

IEEE Trans. Fuzzy Syst.

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This paper is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and the system states, a sequence of proportional-integral observers are established via the system outputs. With the help of a structure separation technique, the proportional-integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniform ultimate boundedness. Simulations are provided to verify the effectiveness of the proposed methods.

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“…(S1) Obtain the values of 0-th observer (x [0] (t),f [0] (t)) by performing (4), then take n = 1. (S2) Getx [n] (t) andf [n] (t) from (6), and calculate…”

Section: Remark 6 Guaranteed By the Stochastic Stability Establishedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Tracking Control for Markov Jump Systems With Mismatched Faults by Iterative Proportional–Integral Observers

Shen¹,

Ma²,

Park

et al. 2022

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

show abstract

“…To overcome this obstacle, a more wider class of systems with general sojourn time distributions, named semi-Markovian jump systems (SMJSs), have been studied in recent years. To mention a few, the problem of stability and stabilization for semi-Markovian jump systems was addressed in [5][6][7]. e controller design problem for semi-Markovian jump systems was investigated in [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Dissipative Filter Design for Semi-Markovian Jump Systems with Time-Varying Delays

Xia

2020

Mathematical Problems in Engineering

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This paper deals with the event-triggered dissipative filtering problem for semi-Markovian jump systems with time-varying delays. The purpose is to design a filter which guarantees that the filtering error system is not only stochastically stable but also satisfies dissipativity. First, based on Lyapunov–Krasovskii theory and matrix integral inequality, a sufficient condition is established for the existence of such a filter. Then, the codesign method of event-triggered matrices and the desired filter parameters is proposed. Finally, two numerical examples are given to illustrate the advantages and validity of the scheme developed in this paper.

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“…This is mainly motivated by the fact that many engineering systems can be described by such kind of systems and examples can be found in aircraft control systems, manufacturing systems, economic systems, and so on 1 . Up to now, numerous results about the stability analysis and essential control/filtering problems have been reported in the literature, for example, References 2‐8 and their references.…”

Section: Introductionmentioning

confidence: 99%

HMM‐based H_∞ filtering for Markov jump systems with partial information and sensor nonlinearities

Zheng

2020

Intl J Robust & Nonlinear

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This work examines the H ∞ filtering issue for Markov jump systems in the circumstances of partial information on Markov chain and randomly occurring sensor nonlinearities. The partial information considered in this work includes partial information on the Markov state, on transition probabilities and on detection probabilities. A hidden Markov model with partially known transition probabilities and detection probabilities is introduced to describe the above partial information phenomenon. The randomly occurring sensor nonlinearities considered in this work depend on the system operating mode. Based on the Lyapunov methodology and the introduced hidden Markov model, some effective H ∞ performance analysis criteria are derived for the filtering error system under the circumstances of partial information and sensor nonlinearities. In addition, the design procedure of the desired hidden Markov model-based filter is established, and finally two examples are used to verify the theoretical results.

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Stabilization of hidden semi-Markov jump systems: Emission probability approach

Cited by 94 publications

References 16 publications

Fuzzy Tracking Control for Markov Jump Systems With Mismatched Faults by Iterative Proportional–Integral Observers

Fuzzy Tracking Control for Markov Jump Systems With Mismatched Faults by Iterative Proportional–Integral Observers

Event-Triggered Dissipative Filter Design for Semi-Markovian Jump Systems with Time-Varying Delays

HMM‐based H_∞ filtering for Markov jump systems with partial information and sensor nonlinearities

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Stabilization of hidden semi-Markov jump systems: Emission probability approach

Cited by 94 publications

References 16 publications

Fuzzy Tracking Control for Markov Jump Systems With Mismatched Faults by Iterative Proportional–Integral Observers

Fuzzy Tracking Control for Markov Jump Systems With Mismatched Faults by Iterative Proportional–Integral Observers

Event-Triggered Dissipative Filter Design for Semi-Markovian Jump Systems with Time-Varying Delays

HMM‐based H∞ filtering for Markov jump systems with partial information and sensor nonlinearities

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Resources

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HMM‐based H_∞ filtering for Markov jump systems with partial information and sensor nonlinearities