Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems
Zhao, Bo1; Li, Yuanchun2; Liu, Derong3
2017-04-26
发表期刊NEUROCOMPUTING
期号235页码:147-156
文章类型Article
摘要In this paper, a decentralized fault tolerant control (DFTC) scheme is proposed for a class of large-scale nonlinear systems based on self-tuned local feedback gain against partial loss of actuator effectiveness (PLOAE). Consider a large-scale nonlinear system as a set of interconnected subsystems, a decentralized control method is proposed by employing two radial basis function neural networks (RBFNNs) for the fault-free system. Then, the unknown system is identified using RBFNNs. By establishing a decentralized observer, the derived self-tuned local feedback gain is placed before the proposed decentralized controller to guarantee control performance for the subsystem suffering from PLOAE fault. Finally, simulation examples are provided to demonstrate the effectiveness of the present DFTC scheme. The main contributions of this paper are: i) The unknown large-scale nonlinear system can be identified using locally measured states, so the actuator fault can be handled in its local subsystem. It implies that the performance degradation of the faulty subsystem cannot affect the fault-free subsystems. ii) The estimated effectiveness factor is placed before the proposed decentralized scheme. The fault tolerant control structure is simple since it does not need to be redesigned in the case of PLOAE.
关键词Large-scale Nonlinear Systems Partial Loss Of Actuator Effectiveness Decentralized Fault Tolerant Control Self-tuned Local Feedback Gain Neural Network
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2016.12.063
关键词[WOS]DYNAMIC SURFACE CONTROL ; INTERCONNECTED SYSTEMS ; FLIGHT CONTROL ; ACTUATOR EFFECTIVENESS ; TRACKING CONTROL ; NEURAL-NETWORK ; CONTROL DESIGN ; IDENTIFICATION ; FAILURES ; VEHICLE
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61233001 ; Scientific and Technological Development Plan Project in Jilin Province of China(20150520112JH ; Beijing Natural Science Foundation(4162065) ; 61273140 ; 20160414033GH) ; 61304086 ; 61374105 ; 61374051 ; 61533017 ; 61503379 ; 61603387 ; U1501251)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000395219700016
引用统计
被引频次:11[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14424
专题复杂系统管理与控制国家重点实验室_平行控制
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Changchun Univ Technol, Dept Control Sci & Engn, Changchun 130012, Peoples R China
3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
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Zhao, Bo,Li, Yuanchun,Liu, Derong. Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems[J]. NEUROCOMPUTING,2017(235):147-156.
APA Zhao, Bo,Li, Yuanchun,&Liu, Derong.(2017).Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems.NEUROCOMPUTING(235),147-156.
MLA Zhao, Bo,et al."Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems".NEUROCOMPUTING .235(2017):147-156.
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