Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics

doi:10.1109/TNNLS.2017.2716947

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics
	Wang, Huanqing 1,2,3; Liu, Peter Xiaoping 2,3; Li, Shuai 4; Wang, Ding5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2018-08-01
卷号	29 期号:8 页码:3658-3668
通讯作者	Wang, Huanqing(ndwhq@163.com) ; Liu, Peter Xiaoping(xpliu@sce.carleton.ca)
摘要	This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.
关键词	Adaptive neural control backstepping nonlower triangular nonlinear systems output-feedback control
DOI	10.1109/TNNLS.2017.2716947
关键词[WOS]	SMALL-GAIN APPROACH ; TRACKING CONTROL ; SURFACE CONTROL ; NETWORK CONTROL ; FUZZY CONTROL ; MIMO SYSTEMS ; DESIGN ; FORM ; APPROXIMATION ; UNCERTAINTIES
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61773051] ; National Natural Science Foundation of China[61773072] ; National Natural Science Foundation of China[61773072] ; National Natural Science Foundation of China[61773051]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000439627700029
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：131[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/173211/26331
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
通讯作者	Wang, Huanqing; Liu, Peter Xiaoping
作者单位	1.Bohai Univ, Dept Math, Jinzhou 121000, Peoples R China 2.Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China 3.Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada 4.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Huanqing,Liu, Peter Xiaoping,Li, Shuai,et al. Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(8):3658-3668.
APA	Wang, Huanqing,Liu, Peter Xiaoping,Li, Shuai,&Wang, Ding.(2018).Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(8),3658-3668.
MLA	Wang, Huanqing,et al."Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.8(2018):3658-3668.