基于扰动观测器的组合状态约束非线性系统模糊有限时间预分配性能控制

IF 3.2 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-02-18 DOI:10.1016/j.fss.2025.109321

Wei Liu , Wei Tang , Huanyu Zhao , Shengyuan Xu , Ju H. Park

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引用次数: 0

摘要

研究了具有输入饱和和不匹配扰动的状态约束非严格反馈非线性系统的模糊有限时间预分配性能控制问题。将barrier Lyapunov函数（BLF）与有限时间性能函数相结合，设计了一种新型的预分配性能BLF来解决状态约束问题，实现预分配性能指标（PPMs）。此外，为了解决NSFNSs的设计问题和估计不匹配的干扰，开发了一种组合非线性干扰观测器。此外，在有限时间控制方案中采用了命令滤波反步方法，从而降低了对期望信号假设的保守性。通过稳定性分析，可以完全获得所有的控制目标。通过两个仿真实例验证了该方法的有效性。

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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Combined disturbance observer-based fuzzy finite-time preassigned performance control for state-constrained nonlinear systems

This paper studies a fuzzy finite-time preassigned performance control challenge for state-constrained non-strict feedback nonlinear systems (NSFNSs) subject to input saturation and unmatched disturbances. By incorporating barrier Lyapunov function (BLF) with finite-time performance function, a new type of preassigned performance BLF is devised to address the issues of state constraints and achieve preassigned performance metrics (PPMs). Moreover, to address the design problem for NSFNSs and estimate unmatched disturbances, a combined nonlinear disturbances observer is developed. Additionally, a command-filter backstepping method is employed in the finite-time control scheme, thereby reducing the conservativeness of the assumptions on the desired signal. With stability analyses, all control aims can be completely acquired. The proposed approach is validated through two simulation examples.

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来源期刊

Fuzzy Sets and Systems 数学-计算机：理论方法

CiteScore

6.50

自引率

17.90%

发文量

321

审稿时长

6.1 months

期刊介绍： Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.