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Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics
Ye D(叶丹)1; Cai, Yujin1; Yang, Haijiao1; Zhao XG(赵新刚)2
Department机器人学研究室
Source PublicationNonlinear Dynamics
ISSN0924-090X
2019
Volume97Issue:1Pages:715-732
Indexed BySCI ; EI
EI Accession number20192807172560
WOS IDWOS:000473520700044
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
KeywordNon-strict feedback Adaptive control Full-state constraints Unmodeled dynamics Input saturation
AbstractIn 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.
Language英语
WOS SubjectEngineering, Mechanical ; Mechanics
WOS KeywordBARRIER LYAPUNOV FUNCTIONS ; NONLINEAR-SYSTEMS ; NETWORK CONTROL ; FUZZY CONTROL
WOS Research AreaEngineering ; Mechanics
Funding ProjectNational Natural Science Foundation of China[61773097] ; National Natural Science Foundation of China[U1813214] ; Fundamental Research Funds for the Central Universities[N160402004]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25324
Collection机器人学研究室
Corresponding AuthorZhao XG(赵新刚)
Affiliation1.College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang 110016, China
Recommended Citation
GB/T 7714
Ye D,Cai, Yujin,Yang, Haijiao,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 Ye D,Cai, Yujin,Yang, Haijiao,&Zhao XG.(2019).Adaptive neural-based control for non-strict feedback systems with full-state constraints and unmodeled dynamics.Nonlinear Dynamics,97(1),715-732.
MLA Ye D,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.
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