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

doi:10.1049/iet-cta.2019.0309

	Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach
	Xiong, Tianyi1,2,3 ; Pu, Zhiqiang1,3 ; Yi, Jianqiang1,3 ; Tao, Xinlong1,3
发表期刊	IET CONTROL THEORY AND APPLICATIONS
ISSN	1751-8644
	2020-06-11
卷号	14 期号:9 页码:1147-1157
通讯作者	Pu, Zhiqiang(zhiqiang.pu@ia.ac.cn)
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.
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
DOI	10.1049/iet-cta.2019.0309
关键词[WOS]	AUTONOMOUS UNDERWATER VEHICLES ; COOPERATIVE CONTROL ; CONSENSUS TRACKING ; MOBILE ROBOTS
收录类别	SCI
语种	英语
资助项目	NNSFC[61603383] ; NNSFC[61421004]
项目资助者	NNSFC
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
WOS类目	Automation & Control Systems ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000538128500004
出版者	INST ENGINEERING TECHNOLOGY-IET
七大方向——子方向分类	多智能体系统
引用统计	被引频次：17[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/173211/39637
专题	复杂系统认知与决策实验室_飞行器智能技术
通讯作者	Pu, Zhiqiang
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing 210037, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.