Robust iterative learning control for Takagi-Sugeno fuzzy nonlinear systems via preview control

IF 5.3 1区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Chaos Solitons & Fractals Pub Date : 2025-03-11 DOI:10.1016/j.chaos.2025.116276

Li Li , Ye Hui , Jia Chen

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引用次数: 0

Abstract

Preview control utilizes future reference signal information to enhance system dynamic response and tracking performance. Iterative learning control represents an effective approach for managing repetitive tasks and has found widespread applications in industrial systems. This paper presents an innovative methodology for developing fuzzy iterative learning preview control using the Takagi-Sugeno (T-S) fuzzy model is discussed. The design incorporates robustness against time-varying uncertainties, and reference tracking capabilities. To accomplish this objective, the T-S fuzzy system is integrated with time-variant uncertainties to establish an augmented error system, thereby transforming the original fuzzy iterative learning preview control problem issue into a stability analysis of the augmented error systems. Subsequently, two distinct fuzzy iterative learning preview control laws are formulated by incorporating the T-S fuzzy system's states or outputs, tracking error, and a previewed reference signal to address the tracking control challenge. Novel sufficient conditions for the asymptotic stability of the augmented error system are derived using the linear matrix inequality (LMI) technique and fuzzy Lyapunov function analysis. Finally, the effectiveness of both proposed control strategies is validated through two numerical examples.

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来源期刊

Chaos Solitons & Fractals 物理-数学跨学科应用

CiteScore

13.20

自引率

10.30%

发文量

1087

审稿时长

9 months

期刊介绍： Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.