Stability and Control of Fuzzy Semi-Markov Jump Systems Under Unknown Semi-Markov Kernel

Ning, Zepeng; Cai, Bo; Weng, Rui; Zhang, Lixian; Su, Shun‐Feng

doi:10.1109/tfuzz.2021.3083959

Cited by 25 publications

(12 citation statements)

References 56 publications

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“…Remark 1: Compared with type-1 T-S fuzzy model [2,3,11,15,16,32], IT2F model is adopted to describe S-MJSs, which makes up for the deficiency of modeling the parameter uncertainty in type-1 fuzzy strategy. In addition, IT2F model can effectively capture the parameter uncertainty by using the upper and lower membership functions, which greatly reduces the conservatism of the research results.…”

Section: A System Descriptionmentioning

confidence: 99%

“…where ℶ(t) = [z T (t), κ T (t), ι(t)] T , and θ > 0 is a constant. According to (11), one has E{ℑV(z(t), ϱ, ι(t))} < 0.…”

Section: A Stochastic Stabilitymentioning

confidence: 99%

“…Compared with MJSs, the ST of sub-modes in S-MJSs follows more general distribution types and the transition rate (TR) among different modes is dependent on the ST, yielding a wider range of applications. In recent years, there have been many achievements in the research of S-MJSs [8][9][10][11][12][13][14].…”

mentioning

confidence: 99%

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Protocol-Based SMC for Interval Type-2 Fuzzy Semi-Markovian Jumping Systems With Channel Fading

Zhang

Park

et al. 2023

IEEE Trans. Fuzzy Syst.

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The sliding mode control (SMC) problem is studied for interval type-2 fuzzy semi-Markovian jumping systems (S-MJSs) subject to channel fading. To reduce the network burden, a dynamic event-triggered protocol is adopted to improve the transmission efficiency. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to random noise and amplitude attenuation during the wireless communication. The main challenge lies in designing an appropriate fuzzy control scheme to achieve the reachability of the specified sliding region in line with channel fading. Under the common sliding surface, a fuzzy SMC law is constructed, which is related to the state signals affected by dynamic event-triggered protocol and channel fading. Then, by means of the boundary information of the global membership functions, sufficient conditions to ensure the stochastic stability of the underlying system, and the system states can be driven on the specified sliding region within a finite-time interval, which attenuates the influence of the channel fading. In the end, the tunnel diode circuit model is simulated to verify the proposed SMC strategy.

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Section: A System Descriptionmentioning

confidence: 99%

“…where ℶ(t) = [z T (t), κ T (t), ι(t)] T , and θ > 0 is a constant. According to (11), one has E{ℑV(z(t), ϱ, ι(t))} < 0.…”

Section: A Stochastic Stabilitymentioning

confidence: 99%

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Protocol-Based SMC for Interval Type-2 Fuzzy Semi-Markovian Jumping Systems With Channel Fading

Zhang

Park

et al. 2023

IEEE Trans. Fuzzy Syst.

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“…T‐S fuzzy models can be applied to design the nonlinear control schemes which will be used to achieve the performance requirements of systems under consideration. This designed schemes for feedback state for T‐S fuzzy control systems will be the useful tools to stabilize the practical nonlinear models; such as autonomous surface vehicle, motor control system, neural network predictive feedback controller, nonlinear modeling problems, a diesel engine controller, sliding mode learning controller, models for truck‐trailer, wind turbine control systems 7–9 . This designing approach will be a good connection for the linear control and the fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Robust (Q, S, R)‐γ‐dissipative control of Takagi–Sugeno fuzzy systems with interval time‐varying delay and sampling

Lien

Chang

et al. 2022

Optim Control Appl Methods

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The robust (Q, S, R)-γ-dissipative control of Takagi-Sugeno fuzzy time-delay model is considered by the developed robust control with sampling in this article. The proposed Lyapunov-Krasovskii functional and inequality are investigated to improve the main results in this article. Full matrix formulation approach is present to show our proposed results by some linear matrix inequalities. Interval delay and sampling period are contemplated instead of constant delay and fixed sampling in some circulated literatures. The less conservatism of our proposed results is demonstrated by our proposed numerical examples. Finally, we illustrate a mass-spring-damper nonlinear system to show our developed results.

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“…Different from the conventional controller, the control design of MJSs is under the idea of switching to improve the performance of closed-loop system. During the past few years, MJSs have been applied to various fields of science and engineering and a great number of research works have been reported [6]- [11] and references therein. However, most of the researcher have only considered the transition probabilities of MJSs are fully known and various results have been investigated in [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Finite-time Sliding Mode Control for Interval Type-II Markov Jump Systems with Partially Known Transition Probabilities under Dynamic Event-triggered Scheme

Jiang

Lin²

2022

JAMCS

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The issue of finite-time event-triggered sliding mode control (SMC) is investigated for a class of interval type- II fuzzy Markov jump systems with partially known transition probabilities. Firstly, for the sake of saving network resources, a dynamic event-triggered scheme (DETS) is proposed to determine whether to transmit the signal or not. Then, a feasible SMC law is developed that makes the state trajectory of the system reach the specified sliding surface in finite-time. Thereafter, by means of the time partition strategy, sufficient conditions for the system to be bounded in finite-time during the arrival and sliding stages are derived. Additionally, the controller gains are computed by utilizing the linear matrix inequality (LMI) toolbox. Lastly, the advantages of the SMC strategy are verified by simulation products.

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Stability and Control of Fuzzy Semi-Markov Jump Systems Under Unknown Semi-Markov Kernel

Cited by 25 publications

References 56 publications

Protocol-Based SMC for Interval Type-2 Fuzzy Semi-Markovian Jumping Systems With Channel Fading

Protocol-Based SMC for Interval Type-2 Fuzzy Semi-Markovian Jumping Systems With Channel Fading

Robust (Q, S, R)‐γ‐dissipative control of Takagi–Sugeno fuzzy systems with interval time‐varying delay and sampling

Finite-time Sliding Mode Control for Interval Type-II Markov Jump Systems with Partially Known Transition Probabilities under Dynamic Event-triggered Scheme

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