Abstract
AbstractThis paper deals with a discrete-time neural observer for flux estimation of a linear induction motor, which is based on a Recurrent High Order Neural Network (RHONN). The RHONN weights are tuned on-line, with no off-line learning phase required, using an extended Kalman filter based algorithm. The observer stability and boundedness of the state estimation and NN weights are proven using the Lyapunov approach. Knowledge of the system model is not required. The applicability of this observer is illustrated by real-time implementation for flux estimation of a linear induction motor benchmark.
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