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<p>This article addresses the challenge of robust tracking control for interval type-2 (IT2) fuzzy systems subjected to external disturbances and time-varying delays. To mitigate communication overhead, a dynamic event-triggered controller based three-layer fully connected feed-forward neural network (TLFCFFNN) controller is introduced, minimizing the frequency of data transmission between the controller and actuator. The proposed approach employs an augmented Lyapunov-Krasovskii functional (LKF) incorporating double and triple integral terms to improve stability analysis. The motivation stems from the presence of parameter uncertainties and the conservatism often observed in conventional output tracking methods. To address these challenges, the study develops a neural network-based control strategy for output trajectory tracking in IT2 fuzzy systems. An improved free-weighting matrix inequality technique is employed to establish a comprehensive tracking criterion. By solving the resulting linear matrix inequalities (LMIs), suitable controller gain matrices are obtained. Numerical simulations and comparative analysis validate the effectiveness and superiority of the proposed method.</p>

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Intelligent tracking control of IT2 fuzzy systems with dynamic event-triggering via neural networks method

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • A. Chandrasekar,
  • T. Radhika,
  • A. R. Subhashri,
  • M. Kamali

摘要

This article addresses the challenge of robust tracking control for interval type-2 (IT2) fuzzy systems subjected to external disturbances and time-varying delays. To mitigate communication overhead, a dynamic event-triggered controller based three-layer fully connected feed-forward neural network (TLFCFFNN) controller is introduced, minimizing the frequency of data transmission between the controller and actuator. The proposed approach employs an augmented Lyapunov-Krasovskii functional (LKF) incorporating double and triple integral terms to improve stability analysis. The motivation stems from the presence of parameter uncertainties and the conservatism often observed in conventional output tracking methods. To address these challenges, the study develops a neural network-based control strategy for output trajectory tracking in IT2 fuzzy systems. An improved free-weighting matrix inequality technique is employed to establish a comprehensive tracking criterion. By solving the resulting linear matrix inequalities (LMIs), suitable controller gain matrices are obtained. Numerical simulations and comparative analysis validate the effectiveness and superiority of the proposed method.