{"title":"Fault-tolerant control design for nonlinear multilateral teleoperation system with unreliable communication channels and actuator constraints","authors":"Huan-Yu Ke, Yang-Jie Chen, Ming Li, Jian-Ning Li","doi":"10.1007/s13042-024-02373-3","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":51327,"journal":{"name":"International Journal of Machine Learning and Cybernetics","volume":"43 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Machine Learning and Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s13042-024-02373-3","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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