Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics

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	Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics
	Zhao XG(赵新刚)1 ; Yang, Haijiao 2; Cai, Yujin 2; Ye D(叶丹)2
刊名	Nonlinear Dynamics
	2019
卷号	97 期号:1 页码:715-732
关键词	Non-strict feedback Adaptive control Full-state constraints Unmodeled dynamics Input saturation
ISSN号	0924-090X
产权排序	2
英文摘要	In this paper, an adaptive neural network controller is designed for non-strict feedback systems with full-state constraints. According to practical applications, both input saturation and unmodeled dynamics are also taken into account. By using a logarithm nonlinear mapping, non-strict feedback systems with full-state constraints can be converted to unconstrained ones, which may result in some exponential terms. Here, a new variable separation method is proposed based on Taylor’s formula to cope with the exponential terms and non-strict structure. Then, the relationship between the norm of state vector and error functions is established. A hyperbolic tangent function and a dynamic signal are introduced to deal with input saturation and unmodeled dynamics, respectively. It is proved that all signals of the closed-loop system are uniformly ultimately bounded and the requirement of full-state constraints is satisfied. Two illustrative examples are provided to demonstrate the effectiveness of the presented method.
资助项目	National Natural Science Foundation of China[61773097] ; National Natural Science Foundation of China[U1813214] ; Fundamental Research Funds for the Central Universities[N160402004]
WOS关键词	BARRIER LYAPUNOV FUNCTIONS ; NONLINEAR-SYSTEMS ; NETWORK CONTROL ; FUZZY CONTROL
WOS研究方向	Engineering ; Mechanics
语种	英语
WOS记录号	WOS:000473520700044
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/25324]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Zhao XG(赵新刚)
作者单位	1.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang 110016, China 2.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
推荐引用方式 GB/T 7714	Zhao XG,Yang, Haijiao,Cai, Yujin,et al. Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics[J]. Nonlinear Dynamics,2019,97(1):715-732.
APA	Zhao XG,Yang, Haijiao,Cai, Yujin,&Ye D.(2019).Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics.Nonlinear Dynamics,97(1),715-732.
MLA	Zhao XG,et al."Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics".Nonlinear Dynamics 97.1(2019):715-732.