Neural Identifier-Control Scheme for Nonlinear Discrete Systems with Input Delay
This work presents a scheme based on a discrete recurrent high order neural network identifier and a block control based on sliding modes for nonlinear discrete-time systems with input delays in real-time. The identifier is trained with an extended Kalman Filter based algorithm and the block control is used for trajectory tracking. Experimental results are included using a linear induction motor prototype with added delays to its input signals.
KeywordsExtended Kalman filter training Neural block control Neural identification Real-time Time-delay
The authors thank the support of CONACYT Mexico, through Projects CB256769 and CB258068 (“Project supported by Fondo Sectorial de Investigación para la Educación”).
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