Discrete-time adaptive backstepping nonlinear control via high-order neural networks

Alma Y Alanis; Edgar N Sanchez; Alexander G Loukianov

doi:10.1109/TNN.2007.899170

Discrete-time adaptive backstepping nonlinear control via high-order neural networks

IEEE Trans Neural Netw. 2007 Jul;18(4):1185-95. doi: 10.1109/TNN.2007.899170.

Authors

Alma Y Alanis¹, Edgar N Sanchez, Alexander G Loukianov

Affiliation

¹ Centro de Investigacion y de Estudios Avanzados del IPN, Unidad Guadalajara, Guadalajara, Jalisco, C.P. 45091, Mexico.

PMID: 17668670
DOI: 10.1109/TNN.2007.899170

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Computer Simulation
Decision Support Techniques*
Feedback
Models, Theoretical*
Neural Networks, Computer*
Nonlinear Dynamics*
Signal Processing, Computer-Assisted*