Dynamic gain adaptive control for uncertain nonlinear systems with actuator and sensor faults

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Changchun Hua, Wenwen Li, Hao Li, Pengju Ning
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引用次数: 0

Abstract

This article focuses on the adaptive output feedback control problem for a class of uncertain nonlinear systems under unknown actuator and sensor faults. In contrast to existing works, we consider unknown time-varying discontinues faults and loosen their conservative conditions on nonlinear functions. First, since there is just inaccurate output information is available, an observer based on dynamic gain is conceived to reconstruct state variables that cannot be measured. Second, the dynamic gain is introduced into the design of virtual controller by coordinate transformation, and is employed to construct Lyapunov function to handle unknown parameters. On the other hand, a new global output feedback control algorithm is proposed by utilizing dynamic gain which improves the flexibility of parameters selection on the premise of ensuring stability. Based on the Lyapunov stability theory, the resulting closed-loop system is proved strictly all signals are global bounded. Finally, a simulation example is presented to illustrate the efficiency of the proposed control algorithm.

具有执行器和传感器故障的不确定非线性系统的动态增益自适应控制
本文主要研究在未知执行器和传感器故障情况下,一类不确定非线性系统的自适应输出反馈控制问题。与现有研究相比,我们考虑了未知时变非连续故障,并放宽了对非线性函数的保守条件。首先,由于只有不准确的输出信息可用,我们设想了一个基于动态增益的观测器来重建无法测量的状态变量。其次,通过坐标变换将动态增益引入虚拟控制器的设计中,并利用动态增益构建 Lyapunov 函数来处理未知参数。另一方面,利用动态增益提出了一种新的全局输出反馈控制算法,在保证稳定性的前提下提高了参数选择的灵活性。基于 Lyapunov 稳定性理论,证明了所得到的闭环系统的所有信号都是全局有界的。最后,通过一个仿真实例说明了所提控制算法的效率。
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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