Command-Filter-Based Fixed-Time Prescribed Tracking Switching Control for Nonlinear Systems With Unknown Control Coefficients

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2025-04-04 DOI:10.1109/TCYB.2025.3554268
Changchun Hua;Wenlong Pan;Hao Li;Qidong Li
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Abstract

This article investigates fixed-time prescribed tracking control based on a command filter for a class of nonlinear systems with unknown control coefficients. A novel switching control mechanism is proposed to address the challenge of unknown control coefficients and introduce a dual-parameter switching strategy with online parameter adjustment based on the designed conditions. To address the limitation in existing research, where prescribed performance functions depend on the initial conditions of systems, this work designs a new class of prescribed performance functions that eliminates this dependency. A command-filter-based backstepping approach effectively avoids the computational complexity of high-order derivatives in traditional backstepping methods. In addition, the issue of the nondifferentiability of the virtual controller at switching moments in existing switching control methods has been resolved. Ultimately, the boundedness of all signals in the closed-loop system is ensured. Moreover, a simulation example of a second-order system verifies the effectiveness of the algorithm in this article.
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基于指令滤波器的未知控制系数非线性系统固定时间规定跟踪切换控制
针对一类控制系数未知的非线性系统,研究了基于命令滤波器的定时预定跟踪控制。针对控制系数未知的挑战,提出了一种新的切换控制机制,并引入了一种基于设计条件的在线参数调整的双参数切换策略。为了解决现有研究中的局限性,其中规定的性能函数依赖于系统的初始条件,本工作设计了一类新的规定的性能函数,消除了这种依赖性。基于命令滤波器的反演方法有效地避免了传统反演方法中高阶导数的计算复杂度。此外,还解决了现有切换控制方法中虚拟控制器在切换时刻不可微的问题。最后,保证了闭环系统中所有信号的有界性。通过一个二阶系统的仿真实例验证了该算法的有效性。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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