Adaptive Fault-Tolerant Control of High-Order Nonlinear Delay Systems Under Unknown Dead-Zone Fault and Unknown Control Coefficients and Its Application

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Xiongfeng Deng, Li Guo, Runze Li
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引用次数: 0

Abstract

This work addresses the fault-tolerant control (FTC) problem of the high-order nonlinear systems (HONSs). The HONSs under study are jointly affected by the unknown time delays, unknown asymmetric dead-zone fault, and unknown control coefficients. Based on the designed Lyapunov-Krasovskii functional (LKF), the unknown time-delay nonlinear terms of the system are effectively compensated. Through the introduction of suitable Nussbaum gain function (NGF), the issue stemming from unknown control coefficients is solved. Meanwhile, the approximation problem of unknown nonlinear dynamics is effectively tackled based on the application of radial basis function neural network (RBFNN). Furthermore, a NN-based adaptive fault-tolerant tracking control scheme is developed and adaptive control laws for some unknown parameters are formulated. The key feature of this control scheme is to ensure that the tracking error asymptotically converges to a small neighborhood of zero, while keeping all signals of the closed-loop system are semi-globally uniformly and ultimately bounded (SGUUB). Ultimately, the validity of the controller proposed in this work is testified through given examples.

未知死区故障和未知控制系数下高阶非线性延迟系统的自适应容错控制及其应用
本文研究了高阶非线性系统的容错控制问题。所研究的HONSs受到未知时滞、未知非对称死区故障和未知控制系数的共同影响。基于所设计的Lyapunov-Krasovskii泛函(LKF),对系统的未知时滞非线性项进行了有效补偿。通过引入合适的Nussbaum增益函数(NGF),解决了控制系数未知引起的问题。同时,应用径向基函数神经网络(RBFNN)有效地解决了未知非线性动力学的逼近问题。在此基础上,提出了一种基于神经网络的自适应容错跟踪控制方案,并给出了一些未知参数的自适应控制规律。该控制方案的关键特点是保证跟踪误差渐近收敛到零的小邻域,同时保持闭环系统的所有信号是半全局一致最终有界的。最后,通过实例验证了所提控制器的有效性。
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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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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