Enhanced state-constrained adaptive fuzzy exact tracking control for nonlinear strict-feedback systems

IF 2.7 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-09-30 DOI:10.1016/j.fss.2025.109598

Qiang Zhang , Dakuo He , Xin Li , Hailong Liu , Xingling Shao

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引用次数: 0

Abstract

An adaptive fuzzy exact tracking control (ETC) is constructed of nonlinear strict-feedback systems with full-state constraints, mismatched external disturbances. Compared with barrier Lyapunov function based on exponential-type constraint, a novel arctan-type constraint is proposed for the first time, whose convergence boundary is consistent with the exponential-type constraint and the initial value has a wider range of applicability under the same parameters. Different from dynamic surface control method and command filter method to solve the “differential explosion” in the backstepping design, the differential of the virtual controller is formulated within a polynomial expression, and an estimation of its maximum value is determined. Subsequently, using this upper bound estimation information, an adaptive fuzzy ETC mechanism is constructed. Utilizing this approach, whenever the tracking error diverges from the origin, an associated control mechanism springs into action, guiding it to converge towards and slide along the origin, thereby ensuring exact tracking control. To demonstrate the salient features of this control mechanism, a nonlinear system and a single-link robotic system are employed in simulation studies.

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非线性严格反馈系统的增强状态约束自适应模糊精确跟踪控制

针对具有全状态约束、外部干扰不匹配的非线性严格反馈系统，构造了一种自适应模糊精确跟踪控制。与基于指数型约束的势垒Lyapunov函数相比，首次提出了一种新的arctantype约束，其收敛边界与指数型约束一致，在相同参数下初值具有更大的适用范围。与解决退步设计中的“差分爆炸”问题的动态曲面控制方法和命令滤波方法不同，虚拟控制器的微分用多项式表达式表示，并确定其最大值的估计。随后，利用该上界估计信息，构造了自适应模糊ETC机制。利用这种方法，每当跟踪误差偏离原点时，一个相关的控制机构就会启动，引导其向原点收敛并沿着原点滑动，从而确保精确的跟踪控制。为了证明该控制机制的显著特点，采用非线性系统和单连杆机器人系统进行了仿真研究。

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

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.