Discrete-time adaptive backstepping nonlinear control via high-order neural networks.
This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.
Centro de Investigacion y de Estudios Avanzados del IPN, Unidad Guadalajara, Guadalajara, Jalisco, C.P. 45091, Mexico.
Alma Y
AY
Alanis
Edgar N
EN
Sanchez
Alexander G
AG
Loukianov
D000465:Algorithms; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted