Title :
Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks
Author :
Alanis, Alma Y. ; Sanchez, Edgar N. ; Loukianov, Alexander G.
Author_Institution :
Unidad Guadalajara, Guadalajara
fDate :
7/1/2007 12:00:00 AM
Abstract :
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.
Keywords :
Kalman filters; Lyapunov methods; MIMO systems; adaptive systems; control nonlinearities; discrete time systems; feedback; neurocontrollers; nonlinear control systems; nonlinear filters; stability; Lyapunov approach; NN learning algorithm; adaptive tracking; block strict feedback form; bounded disturbances; control law; discrete-time adaptive backstepping nonlinear control; discrete-time multiple-input-multiple-output nonlinear system; electric induction motor; extended Kalman filter; high-order neural networks; stability analysis; Adaptive control; Backstepping; Control systems; Induction motors; MIMO; Neural networks; Neurofeedback; Nonlinear systems; Programmable control; Stability analysis; Backstepping; discrete-time systems; electric induction motor; extended Kalman filtering (EKF); high-order neural networks (HONNs); Algorithms; Computer Simulation; Decision Support Techniques; Feedback; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics; Signal Processing, Computer-Assisted;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.899170
