Back to Search Start Over

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

Authors :
Zhang, Yanjun
Tao, Gang
Chen, Mou
Chen, Wen
Zhang, Zhengqiang
Source :
IEEE Transactions on Cybernetics; Dec2021, Vol. 51 Issue 12, p5728-5739, 12p
Publication Year :
2021

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21682267
Volume :
51
Issue :
12
Database :
Complementary Index
Journal :
IEEE Transactions on Cybernetics
Publication Type :
Academic Journal
Accession number :
154265721
Full Text :
https://doi.org/10.1109/TCYB.2019.2958844