Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults

doi:10.1080/00207721.2020.1817615

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults
	Ji, Ning 1; Liu, Jinkun 1; Yang, Hongjun 2
刊名	INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
	2020-09-10
页码	11
关键词	Underactuated system sliding mode control actuator fault tolerance sensor fault tolerance coupled motor driving system
ISSN号	0020-7721
DOI	10.1080/00207721.2020.1817615
通讯作者	Liu, Jinkun(ljk@buaa.edu.cn)
英文摘要	A sliding mode control method is developed in this study for application to a class of underactuated systems with bounded unknown disturbance and sensor and actuator faults. In the proposed method, a robustness item compensates for the bounded unknown disturbance and a Nussbaum function realises sensor and actuator faults tolerance simultaneously, and all signals of the system are proven to be bounded. A radial basis function (RBF) neural network is developed to estimate the unknown functions of the system. Finally, Hurwitz stability analysis is conducted to guarantee the stability of the closed-loop system. Simulations are conducted wherein a coupled motor driving system is placed under the proposed control laws to validate this approach.
资助项目	National Natural Science Foundation of China[61873296] ; Academic Excellence Foundation of BUAA ; CAS Prospective Deployment Project[ZDRW-KT2019-1-010402]
WOS关键词	NONLINEAR-SYSTEMS ; TRACKING CONTROL ; CONTROL STRATEGY ; DESIGN
WOS研究方向	Automation & Control Systems ; Computer Science ; Operations Research & Management Science
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000567974700001
资助机构	National Natural Science Foundation of China ; Academic Excellence Foundation of BUAA ; CAS Prospective Deployment Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/41934]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Liu, Jinkun
作者单位	1.Beihang Univ, Sch Automat Sci & Elect Engn, Beijing, Peoples R China 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Ji, Ning,Liu, Jinkun,Yang, Hongjun. Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults[J]. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE,2020:11.
APA	Ji, Ning,Liu, Jinkun,&Yang, Hongjun.(2020).Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults.INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE,11.
MLA	Ji, Ning,et al."Sliding mode control based on RBF neural network for a class of underactuated systems with unknown sensor and actuator faults".INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE (2020):11.