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Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback
Wang H(王昊); Wang D(王丹); Peng ZH(彭周华)
Department水下机器人研究室
Source PublicationInternational Journal of Systems Science
ISSN0020-7721
2016
Volume47Issue:2Pages:343–359
Indexed BySCI ; EI
EI Accession number20152600983026
WOS IDWOS:000360553200006
Contribution Rank1
Funding OrganizationNational Nature ScienceFoundation of China [grant number 61273137], [grant number51209026] ; the China Postdoctoral Science Foundation [grantnumber 2015M570247] ; the Scientific Research Fund of LiaoningProvincial Education Department [grant number L2013202] ; and the Fundamental Research Funds for the Central Universities[grant number 3132015021], [grant number 3132014321].
KeywordCooperative Path Following Marine Surface Vehicles Neural Networks Observer Uncertainties
AbstractThis paper addresses the cooperative path-following problem of multiple marine surface vehicles subject to dynamical uncertainties and ocean disturbances induced by unknown wind, wave and ocean current. The control design falls neatly into two parts. One is to steer individual marine surface vehicle to track a predefined path and the other is to synchronise the along-path speed and path variables under the constraints of an underlying communication network. Within these two formulations, a robust adaptive path-following controller is first designed for individual vehicles based on backstepping and neural network techniques. Then, a decentralised synchronisation control law is derived by means of consensus on along-path speed and path variables based on graph theory. The distinct feature of this design lies in that synchronised path following can be reached for any undirected connected communication graphs without accurate knowledge of the model. This result is further extended to the output feedback case, where an observer-based cooperative path-following controller is developed without measuring the velocity of each vehicle. For both designs, rigorous theoretical analysis demonstrate that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Simulation results validate the performance and robustness improvement of the proposed strategy.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectAutomation & Control Systems ; Computer Science, Theory & Methods ; Operations Research & Management Science
WOS KeywordUNCERTAIN NONLINEAR-SYSTEMS ; MODEL UNCERTAINTY ; VESSELS ; TRACKING ; FORM
WOS Research AreaAutomation & Control Systems ; Computer Science ; Operations Research & Management Science
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/16466
Collection水下机器人研究室
Corresponding AuthorWang D(王丹)
Affiliation1.Marine Engineering College, Dalian Maritime University, Dalian, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Recommended Citation
GB/T 7714
Wang H,Wang D,Peng ZH. Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback[J]. International Journal of Systems Science,2016,47(2):343–359.
APA Wang H,Wang D,&Peng ZH.(2016).Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback.International Journal of Systems Science,47(2),343–359.
MLA Wang H,et al."Adaptive neural control for cooperative path following of marine surface vehicles: state and output feedback".International Journal of Systems Science 47.2(2016):343–359.
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