Recurrent-neural-network-based adaptive-backstepping control for induction servomotors

Abstract

This study is concerned with the position control of an induction servomotor using a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) system. The adaptive-backstepping approach offers a choice of design tools for the accommodation of system uncertainties and nonlinearities. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. Since the RNN has superior capabilities compared to the feed-forward NN for dynamic system identification, it is utilized as the uncertainty observer. In addition, the Taylor linearization technique is employed to increase the learning ability of the RNN. Meanwhile, the adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Finally, simulation and experimental results verify that the proposed RNABC can achieve favorable tracking performance for the induction-servomotor system, even with regard to parameter variations and input-command frequency variation.