Tracking control for restrained nonlinear systems with time-changing latency using the preview control approach

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-08-07 DOI:10.1002/rnc.7577

Li Li, Yanrong Lu, Yaofeng Zhang

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引用次数: 0

Abstract

An innovative technique for developing fuzzy preview tracking control nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is discussed. The design considers robustness against state time-changing latency, input saturation, and reference tracking. To achieve this, the T-S fuzzy system was combined with time-changing latency and input saturation to create an augmented error system, transforming the original fuzzy preview tracking control issue into a stability issue for the augmented error systems. Next, a new fuzzy preview tracking controller was developed by considering the T-S fuzzy systems' states or outputs, a distinct integrator, and a previewed reference signal to solve the tracking control issue. For the augmented error system's asymptotic stability, new adequate conditions were derived by employing the fuzzy Lyapunov function, small gain theory, and linear matrix inequality (LMI) method. Finally, the proposed method's effectiveness was demonstrated using two numerical examples.

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使用预览控制方法对具有时变延迟的约束非线性系统进行跟踪控制

本文讨论了一种利用高木-菅野（Takagi-Sugeno，T-S）模糊模型开发模糊预览跟踪控制非线性系统的创新技术。该设计考虑了状态时变延迟、输入饱和和参考跟踪的鲁棒性。为此，T-S 模糊系统与时变延迟和输入饱和相结合，创建了一个增强误差系统，将原来的模糊预览跟踪控制问题转化为增强误差系统的稳定性问题。接下来，通过考虑 T-S 模糊系统的状态或输出、独特的积分器和预览参考信号，开发了一种新的模糊预览跟踪控制器，以解决跟踪控制问题。通过使用模糊 Lyapunov 函数、小增益理论和线性矩阵不等式（LMI）方法，得出了增强误差系统渐近稳定性的新充分条件。最后，利用两个数值实例证明了所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.