Real-time implementation of a neural inverse optimal control for a linear induction motor

Abstract

This paper presents the real-time application of a discrete-time inverse optimal control to a three-phase linear induction motor (LIM) in order to achieve trajectory tracking of a position reference. A recurrent high-order neural network (RHONN) is employed on-line to determine the model of the motor. The equipment and software employed are described as well as real-time trajectory tracking results. © 2014 Springer International Publishing Switzerland.