Discrete-Time Recurrent High Order Neural Observer for Induction Motors
A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability simulation results are included.
KeywordsInduction Motor Load Torque Rotor Resistance Nonlinear Observer MIMO Nonlinear System
Unable to display preview. Download preview PDF.
- 1.Ge, S.S., Zhang, J., Lee, T.H.: Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time. IEEE Transactions on Systems, Man and Cybernetics, Part B 34(4) (2004)Google Scholar
- 4.Khorrami, F., Krishnamurthy, P., Melkote, H.: Modeling and Adaptive Nonlinear Control of Electric Motors. Springer, New York (2003)Google Scholar
- 7.Loukianov, A.G., Rivera, J., Cañedo, J.M.: Discrete-time sliding mode control of an induction motor. In: Proceedings IFAC’02, Barcelone, Spain (July 2002)Google Scholar
- 11.Sanchez, E.N., Alanis, A.Y., Chen, G.: Recurrent neural networks trained with Kalman filtering for discrete chaos reconstruction. In: Proceedings of Asian-Pacific Workshop on Chaos Control and Synchronization’04, Melbourne, Australia (July 2004)Google Scholar
- 12.Ricalde, L.J., Sanchez, E.N.: Inverse optimal nonlinear high order recurrent neural observer. In: International Joint Conference on Neural Networks IJCNN 05, Montreal, Canada (August 2005)Google Scholar
- 13.Rovithakis, G.A., Chistodoulou, M.A.: Adaptive Control with Recurrent High -Order Neural Networks. Springer, New York (2000)Google Scholar