TY - JOUR
T1 - An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems
AU - Zhang, Yanjun
AU - Tao, Gang
AU - Chen, Mou
AU - Chen, Wen
AU - Zhang, Zhengqiang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
KW - Adaptive control
KW - Asymptotic output tracking
KW - Discrete time (DT)
KW - Implicit function
KW - Noncanonical form
UR - http://www.scopus.com/inward/record.url?scp=85096097279&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2019.2958844
DO - 10.1109/TCYB.2019.2958844
M3 - Article
C2 - 31940572
AN - SCOPUS:85096097279
SN - 2168-2267
VL - 51
SP - 5728
EP - 5739
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 12
ER -