Sliding Mode Fault-Tolerant Control for Nonlinear High-Order Fully Actuated Systems

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-10-25 DOI:10.1109/TCYB.2024.3482320
Yuqi Jiang;Qian Wang;Guoda Chen;Zhengguang Wu
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Abstract

The high-order fully actuated systems (HOFASs) approach can only completely eliminate known nonlinearities. However, the practical systems often encounter unknown nonlinearities, including disturbances and faults. Therefore, this article studies a class of nonlinear HOFAS with component faults and disturbances. By applying the HOFAS theory, a novel integrated sliding mode fault-tolerant control strategy is proposed to ensure the stability of the closed-loop system. The state feedback and output feedback controllers are designed, respectively. For output feedback control, an extended state observer is designed to estimate system states. Once the HOFAS model is established, the designed controller and extended state observer can be directly implemented, facilitating the analysis and design of the control system. And the stability analysis does not depend on the complexity of the nonlinear functions. Finally, a numerical simulation example shows the effectiveness of the proposed method.
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非线性高阶全致动系统的滑动模式容错控制
高阶全驱动系统(HOFASs)方法只能完全消除已知的非线性。然而,实际系统经常遇到未知的非线性,包括干扰和故障。因此,本文研究了一类具有分量故障和扰动的非线性HOFAS。应用HOFAS理论,提出了一种新的集成滑模容错控制策略,以保证闭环系统的稳定性。分别设计了状态反馈控制器和输出反馈控制器。对于输出反馈控制,设计了扩展状态观测器来估计系统状态。一旦建立了HOFAS模型,就可以直接实现所设计的控制器和扩展状态观测器,便于控制系统的分析和设计。并且稳定性分析不依赖于非线性函数的复杂度。最后,通过数值仿真验证了该方法的有效性。
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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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