Optimal output feedback event-triggered tracking control for Takagi-Sugeno fuzzy systems

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

Fuzzy Sets and Systems Pub Date : 2025-08-13 DOI:10.1016/j.fss.2025.109565

Wenting Song , Yi Zuo , Shaocheng Tong

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引用次数: 0

Abstract

This paper studies the optimal output feedback event-triggered tracking control design problem for Takagi-Sugeno (T-S) fuzzy systems. To reduce the communication resources and controller update times, an event-triggered mechanism is designed via employing the tracking error and triggered control input signal. Based on the presented event-triggered mechanism and optimality theory, an optimal output feedback event-triggered tracking controller is developed. Since the analytical solution of the controller gains is reduced to the Agebraic Riccati Equations (AREs), which is difficult to solve directly, a Q-learning value iteration (VI) algorithm is formulated to obtain its approximation solution. It is proved that the designed optimal output feedback event-triggered tracking controller can ensure the fuzzy system to be stable and the developed Q-learning VI control algorithm is convergent. Finally, we apply the proposed optimal event-triggered output feedback control method to the truck-trailer system, the simulation and comparison results validate the effectiveness of the designed control method and its theory.

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Takagi-Sugeno模糊系统的最优输出反馈事件触发跟踪控制

研究了Takagi-Sugeno （T-S）模糊系统的最优输出反馈事件触发跟踪控制设计问题。为了减少通信资源和控制器更新次数，设计了一种利用跟踪误差和触发控制输入信号的事件触发机制。基于所提出的事件触发机制和最优性理论，提出了一种最优输出反馈事件触发跟踪控制器。由于控制器增益的解析解被简化为难以直接求解的agbraic Riccati方程组（AREs），因此提出了Q-learning值迭代（VI）算法来获得其近似解。结果表明，所设计的最优输出反馈事件触发跟踪控制器能够保证模糊系统的稳定，所开发的Q-learning VI控制算法具有收敛性。最后，将所提出的最优事件触发输出反馈控制方法应用于卡车-挂车系统，仿真和对比结果验证了所设计控制方法及其理论的有效性。

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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.