Fault-tolerant control design for nonlinear multilateral teleoperation system with unreliable communication channels and actuator constraints

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Machine Learning and Cybernetics Pub Date : 2024-09-05 DOI:10.1007/s13042-024-02373-3
Huan-Yu Ke, Yang-Jie Chen, Ming Li, Jian-Ning Li
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Abstract

For nonlinear multilateral teleoperation systems, unreliable communication channels and actuator constraints are the main challenging issues to achieve the stability condition and satisfy the required performance. In this paper, a novel fault-tolerant control algorithm is proposed for a class of multi-degree-of-freedom nonlinear multilateral teleoperation systems with the aforementioned problems and unknown environmental forces. The time-varying delays and packet dropouts are incorporated in the unreliable communication channels, and the considered systems are modeled as a kind of T-S fuzzy systems with multiple time-varying delays. For actuator constraints, both the actuator failures and the unknown control directions are investigated in such research, by designing a novel fault-tolerant control scheme, the failures and control directions can be estimated simultaneously. Next, the radial basis function neural network (RBFNN) is introduced to estimate the unknown environmental force, and the estimated results are incorporated in the controller design and the mean-square stability of the closed-loop system with disturbance attenuation level is guaranteed. Finally, a numerical simulation example is given to show the effectiveness of the proposed method.

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具有不可靠通信信道和执行器约束条件的非线性多边远程操纵系统的容错控制设计
对于非线性多边遥控系统,要达到稳定条件并满足所需的性能,不可靠的通信信道和执行器约束是主要的挑战性问题。本文针对存在上述问题和未知环境力的一类多自由度非线性多边遥控系统,提出了一种新型容错控制算法。在不可靠的通信信道中加入了时变延迟和丢包,并将所考虑的系统建模为一种具有多个时变延迟的 T-S 模糊系统。通过设计一种新型容错控制方案,可以同时估计故障和控制方向。接着,引入径向基函数神经网络(RBFNN)来估计未知环境力,并将估计结果纳入控制器设计,保证了具有扰动衰减水平的闭环系统的均方稳定性。最后,给出了一个数值模拟实例,以说明所提方法的有效性。
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来源期刊
International Journal of Machine Learning and Cybernetics
International Journal of Machine Learning and Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.90
自引率
10.70%
发文量
225
期刊介绍: Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life. Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems. The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data. The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area. The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications. New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC. Key research areas to be covered by the journal include: Machine Learning for modeling interactions between systems Pattern Recognition technology to support discovery of system-environment interaction Control of system-environment interactions Biochemical interaction in biological and biologically-inspired systems Learning for improvement of communication schemes between systems
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