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

Yanjun Zhang, Gang Tao, Mou Chen, Wen Chen, Zhengqiang Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5728-5739
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume51
Issue number12
DOIs
Publication statusPublished - 1 Dec 2021
Externally publishedYes

Keywords

  • Adaptive control
  • Asymptotic output tracking
  • Discrete time (DT)
  • Implicit function
  • Noncanonical form

Fingerprint

Dive into the research topics of 'An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems'. Together they form a unique fingerprint.

Cite this