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Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed
Wang H(王昊); Liu KZ(刘开周); Li S(李硕)
作者部门海洋机器人卓越创新中心
关键词Cooperative Path Following Uncertainties Autonomous Underwater Vehicles Neural Networks
发表期刊Neurocomputing
ISSN0925-2312
2018
卷号275页码:1478-1489
收录类别SCI ; EI
EI收录号20174304289795
WOS记录号WOS:000418370200139
产权排序1
资助机构National Key Research and Development Program of China under Grants 2016YFC0300801, 2016YFC0301600, 2016YFC0300604, 2017YFC0305901, and in part by the National Natural Science Foundation of China under Grants 61233013, 41376110, 41106085, and in part by the National HighTech Research and Development Program of China under Grant 2014AA09A110, and in part by the Public Science and Technology Research Funds Projects of Ocean under Grant 201505017, and in part by the Instrument Developing Project of the Chinese Academy of Sciences under Grant YZ201441, and in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA13030203, and in part by the Youth Innovation Promotion Association CAS under Grant 2011161, and in part by the Laboratory Foundation of Science and Technology on Water Jet Propulsion under Grant 61422230302162223012, and in part by the Doctoral Scientific Research Foundation of Liaoning Province under Grant 201501035, and in part by the State Key Laboratory of Robotics under Grants 2016-Z02
摘要This paper investigates the problem of cooperative path following for a fleet of underactuated autonomous underwater vehicles (AUVs) with uncertain nonlinear dynamics. Path following controllers for individual AUVs are developed to ensure that each AUV converges to the desired position. The coordination mission is completed by reaching synchronization on a suitably defined path variable, even in the presence of partial knowledge of the reference speed. The key features of the proposed cooperative path following design scheme can be summarized as follows. First, the command filter design technique based cooperative path following control strategy is derived by introducing compensating error signals to remove the requirement of the higher derivative of reference signal, and a simplified cooperative path following controller is proposed. Second, a smoothly switching function is designed to yield neural network (NN) based energy-efficient controller. Third, by designing the distributed speed estimator, the global knowledge of the reference speed is relaxed. Finally, all the signals in the closed-loop system are guaranteed to be globally uniformly ultimately bounded (GUUB) under the proposed algorithm, and the path following error is proven to converge to a small neighborhood of the origin. Simulation example is provided to validate the performance of the control strategy.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence
关键词[WOS]DYNAMIC SURFACE CONTROL ; UNCERTAIN NONLINEAR-SYSTEMS ; OUTPUT-FEEDBACK CONTROL ; TRACKING CONTROL ; VESSELS ; LEADER ; ENVIRONMENTS ; NETWORKS ; ROBOTS ; FORM
WOS研究方向Computer Science
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/21219
专题海洋机器人卓越创新中心
通讯作者Li S(李硕)
作者单位State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
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GB/T 7714
Wang H,Liu KZ,Li S. Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed[J]. Neurocomputing,2018,275:1478-1489.
APA Wang H,Liu KZ,&Li S.(2018).Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed.Neurocomputing,275,1478-1489.
MLA Wang H,et al."Command filter based globally stable adaptive neural control for cooperative path following of multiple underactuated autonomous underwater vehicles with partial knowledge of the reference speed".Neurocomputing 275(2018):1478-1489.
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