Adaptive recurrent-neural-network control for linear induction motor

In this study an adaptive recurrent-neural-network controller (ARNNC) is proposed to control a linear induction motor (LIM) servo drive. First, the secondary flux of the LEVI is estimated with an adaptive flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, an ARNNC is proposed to control the mover of the LIM for periodic motion. In the proposed controller, the LIM servo drive system is identified by a recurrent-neural-network identifier (RNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the RNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RNNI and the optimal learning rate of the adaptive controller. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system.