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<p>Preview control and iterative learning control are both effective strategies for improving tracking performance in systems modeled by Takagi–Sugeno (T–S) fuzzy frameworks, particularly when dealing with previewable reference signals. This study introduces a methodology for designing fuzzy iterative learning preview control (ILPC) for nonlinear systems using T–S fuzzy models. To address the combined effects of time-varying delay and the previewed reference signal, an augmented error model (AEM) is constructed. This reformulation transforms the original fuzzy ILPC problem into a stability analysis problem for the AEM. Subsequently, a novel fuzzy tracking controller is developed by incorporating the system’s states or outputs, tracking error, and previewed reference signal. To ensure convergence of the AEM, new sufficient stability conditions are derived using a fuzzy Lyapunov–Krasovskii functional combined with the linear matrix inequality (LMI) framework. Finally, the effectiveness of the proposed method for controlling T–S fuzzy models with time-varying delays is demonstrated using Chua’s circuit model. Fuzzy ILPC has broad application potential in high-precision motion control systems, including robotic manipulators and CNC machining, where repetitive tasks require increasingly accurate trajectory tracking over successive iterations.</p>

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Iterative Learning Preview Control of T–S Fuzzy System Via Time-Varying Delay

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Li Li,
  • Jiang Wu,
  • Tao Jiang

摘要

Preview control and iterative learning control are both effective strategies for improving tracking performance in systems modeled by Takagi–Sugeno (T–S) fuzzy frameworks, particularly when dealing with previewable reference signals. This study introduces a methodology for designing fuzzy iterative learning preview control (ILPC) for nonlinear systems using T–S fuzzy models. To address the combined effects of time-varying delay and the previewed reference signal, an augmented error model (AEM) is constructed. This reformulation transforms the original fuzzy ILPC problem into a stability analysis problem for the AEM. Subsequently, a novel fuzzy tracking controller is developed by incorporating the system’s states or outputs, tracking error, and previewed reference signal. To ensure convergence of the AEM, new sufficient stability conditions are derived using a fuzzy Lyapunov–Krasovskii functional combined with the linear matrix inequality (LMI) framework. Finally, the effectiveness of the proposed method for controlling T–S fuzzy models with time-varying delays is demonstrated using Chua’s circuit model. Fuzzy ILPC has broad application potential in high-precision motion control systems, including robotic manipulators and CNC machining, where repetitive tasks require increasingly accurate trajectory tracking over successive iterations.