Title :
Discrete- Time Recurrent Neural Induction Motor Control using Kalman Learning
Author :
Alanis, Alma Y. ; Sanchez, Edgar N. ; Loukianov, Alexander G.
Author_Institution :
CINVESTAV, Guadalajara
Abstract :
This paper deals with the adaptive tracking problem for discrete-time induction motor model in presence of bounded disturbances. In this paper, a high order neural network structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The paper also includes the respective stability analysis, for the whole system with a strategy to avoid specific adaptive weights zero-crossing. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.
Keywords :
discrete time systems; induction motors; learning (artificial intelligence); machine control; nonlinear control systems; recurrent neural nets; variable structure systems; Kalman learning; adaptive tracking problem; discrete-time block control; discrete-time recurrent neural induction motor control; nonlinear model; plant model; sliding modes techniques; stability analysis; Adaptive control; Control systems; Induction motors; Kalman filters; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Recurrent neural networks; Sliding mode control; Discrete-time sliding mode control; Extended Kalman filtering; Induction Motor; Neural block control; Recurrent high order neural networks;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.246946
