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Double Asynchronous Switching Control for Takagi–Sugeno Fuzzy Markov Jump Systems via Adaptive Event-Triggered Mechanism

Yinghong Zhao, Likui Wang, Xiangpeng Xie, Jiayue Hou, Hak‐Keung Lam

2024IEEE Transactions on Systems Man and Cybernetics Systems11 citationsDOIOpen Access PDF

Abstract

This article addresses the issue of adaptive event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H_{\infty} }$</tex-math> </inline-formula> control for Markov jump systems based on the Takagi–Sugeno (T–S) fuzzy model. First, a new double asynchronous switching controller is presented to deal with the problem of the mismatch of premise variables and modes between the controller and the plant, which is widespread in real network environment. To further reduce the power consumption of communication, a switching adaptive event-triggered mechanism is adopted to relieve the network transmission pressure while ensuring the control effect. In addition, a new Lyapunov–Krasovskii functional (LKF) is constructed to reduce conservatism by introducing the membership functions (MFs) and time-varying delays information. Meanwhile, the invariant set is estimated to ensure the stability of the system. And the disturbance rejection ability is measured by the optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${H_{\infty} }$</tex-math> </inline-formula> performance index. Finally, two examples are presented to demonstrate the effectiveness of the proposed approach.

Topics & Concepts

Asynchronous communicationController (irrigation)Control theory (sociology)NotationComputer scienceEvent (particle physics)Markov chainAdaptive controlFuzzy logicJumpMathematicsControl (management)Artificial intelligenceMachine learningArithmeticBiologyPhysicsComputer networkAgronomyQuantum mechanicsNeural Networks Stability and SynchronizationStability and Control of Uncertain SystemsDistributed Control Multi-Agent Systems
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