Dynamic-surface-based adaptive predefined-time control for nonlinear non-affine switched systems with sensor fault

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Ke Xu, Huanqing Wang, Peter Xiaoping Liu
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引用次数: 0

Abstract

The adaptive neural tracking fault-tolerant control problem is considered for nonlinear non-affine switched systems with sensor faults via dynamic surface control (DSC) technique under arbitrary switchings within predefined-time interval. During the controller design process, the non-affine formation strictly processed so that the implicit control signals can be transformed into explicit ones. The introduction of hyperbolic tangent function to design the control signal eliminates the singularity at the same time, but also avoids the tedious discussion of the segmentation function to solve the singularity. Considering Lyapunov stability theorem, an adaptive fault tolerant control approach is presented, which means that the settling-time can be programmed by the user practical specification under arbitrary switching, the predefined time boundedness of all closed-loop signals can be ensured, and the influence of sensor faults can be compensated. The effectiveness of the presented method is verified via simulation results.

带传感器故障的非线性非参量开关系统的基于动态曲面的自适应预定义时间控制
通过动态表面控制(DSC)技术,在预定时间间隔内任意切换的情况下,考虑了具有传感器故障的非线性非参量切换系统的自适应神经跟踪容错控制问题。在控制器设计过程中,严格处理非参量形成,以便将隐式控制信号转换为显式控制信号。引入双曲正切函数设计控制信号,在消除奇异性的同时,也避免了为解决奇异性而对分段函数进行繁琐的讨论。考虑到 Lyapunov 稳定性定理,提出了一种自适应容错控制方法,即在任意切换情况下,沉降时间可根据用户实际规格进行编程,确保所有闭环信号的预定时间约束性,并可补偿传感器故障的影响。仿真结果验证了所介绍方法的有效性。
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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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