An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems

doi:10.1109/TCYB.2019.2958844

	An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems
	Zhang, Yanjun 1,2; Tao, Gang 5; Chen, Mou 3; Chen, Wen 4; Zhang, Zhengqiang 2
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2021-12-01
卷号	51 期号:12 页码:5728-5739
关键词	Adaptive control Uncertainty Nonlinear systems Adaptation models Asymptotic stability Stability analysis Adaptive control asymptotic output tracking discrete time (DT) implicit function noncanonical form
ISSN号	2168-2267
DOI	10.1109/TCYB.2019.2958844
英文摘要	This article proposes a new implicit function-based adaptive control scheme for the discrete-time neural-network systems in a general noncanonical form. Feedback linearization for such systems leads to the output dynamics nonlinear dependence on the system states, the control input, and uncertain parameters, which leads to the nonlinear parametrization problem, the implicit relative degree problem, and the difficulty to specify an analytical adaptive controller. To address these problems, we first develop a new adaptive parameter estimation strategy to deal with all uncertain parameters, especially, those of nonlinearly parameterized forms, in the output dynamics. Then, we construct a key implicit function equation using available signals and parameter estimates. By solving the equation, a unique adaptive control law is derived to ensure asymptotic output tracking and closed-loop stability. Alternatively, we design an iterative solution-based adaptive control law which is easy to implement and ensure output tracking and closed-loop stability. The simulation study is given to demonstrate the design procedure and verify the effectiveness of the proposed adaptive control scheme.
资助项目	National Key R&D Program of China[2018YFA0703800] ; National Natural Science Foundation of China[61803226] ; National Natural Science Foundation of China[61533009] ; National Natural Science Foundation of China[61873330] ; National Natural Science Foundation of China[61877057] ; Taishan Scholarship Project of Shandong Province[tsqn20161032]
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000733232400012
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/59784]
专题	中国科学院数学与系统科学研究院
通讯作者	Zhang, Zhengqiang
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 2.Qufu Normal Univ, Sch Engn, Qufu 273165, Shandong, Peoples R China 3.Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Peoples R China 4.Wayne State Univ, Div Engn Technol, Detroit, MI 48201 USA 5.Univ Virginia, Dept Elect & Comp Engn, Charlottesville, VA 22903 USA
推荐引用方式 GB/T 7714	Zhang, Yanjun,Tao, Gang,Chen, Mou,et al. An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021,51(12):5728-5739.
APA	Zhang, Yanjun,Tao, Gang,Chen, Mou,Chen, Wen,&Zhang, Zhengqiang.(2021).An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems.IEEE TRANSACTIONS ON CYBERNETICS,51(12),5728-5739.
MLA	Zhang, Yanjun,et al."An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems".IEEE TRANSACTIONS ON CYBERNETICS 51.12(2021):5728-5739.