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Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach

文献类型：期刊论文

作者Xiong, Tianyi1,2,3; Pu, Zhiqiang2,3; Yi, Jianqiang2,3; Tao, Xinlong2,3
刊名IET CONTROL THEORY AND APPLICATIONS
出版日期2020-06-11
卷号14期号:9页码:1147-1157
关键词neurocontrollers multi-agent systems Lyapunov methods closed loop systems nonlinear control systems time-varying systems adaptive control observers uncertain systems position control radial basis function networks robust control control system synthesis learning (artificial intelligence) minimal learning-parameter approach fixed-time CLSO time-varying formation tracking problem formation tracking control scheme multiagent systems time-varying formation tracking control problem model uncertainties velocity measurements radial basis function neural networks fixed-time cascaded leader state observer fixed-time observer-based adaptive neural network time-varying formation tracking control RBFNN-based adaptive control scheme
ISSN号1751-8644
DOI10.1049/iet-cta.2019.0309
通讯作者Pu, Zhiqiang(zhiqiang.pu@ia.ac.cn)
英文摘要This study proposes a novel control scheme to investigate the time-varying formation tracking control problem for multi-agent systems with model uncertainties and the absence of leader's velocity measurements. For each agent, a novel fixed-time cascaded leader state observer (CLSO) without velocity measurements is first designed to reconstruct the states of the leader. Radial basis function neural networks (RBFNNs) are adopted to deal with the model uncertainties online. Taking the square of the norm of the NN weight vector as a newly developed adaptive parameter, a novel RBFNN-based adaptive control scheme with minimal learning-parameter approach and fixed-time CLSO is then constructed to tackle the time-varying formation tracking problem. The uniform ultimate boundedness property of the formation tracking error is guaranteed through Lyapunov stability analysis. Finally, two simulation scenario results demonstrate the effectiveness of the proposed formation tracking control scheme.
WOS关键词AUTONOMOUS UNDERWATER VEHICLES ; COOPERATIVE CONTROL ; CONSENSUS TRACKING ; MOBILE ROBOTS
资助项目NNSFC[61603383] ; NNSFC[61421004]
WOS研究方向Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000538128500004
出版者INST ENGINEERING TECHNOLOGY-IET
资助机构NNSFC
源URL[http://ir.ia.ac.cn/handle/173211/39636]  
专题综合信息系统研究中心_飞行器智能技术
通讯作者Pu, Zhiqiang
作者单位1.Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing 210037, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xiong, Tianyi,Pu, Zhiqiang,Yi, Jianqiang,et al. Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach[J]. IET CONTROL THEORY AND APPLICATIONS,2020,14(9):1147-1157.
APA Xiong, Tianyi,Pu, Zhiqiang,Yi, Jianqiang,&Tao, Xinlong.(2020).Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach.IET CONTROL THEORY AND APPLICATIONS,14(9),1147-1157.
MLA Xiong, Tianyi,et al."Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach".IET CONTROL THEORY AND APPLICATIONS 14.9(2020):1147-1157.

入库方式： OAI收割

来源：自动化研究所

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