Radial basis function neural network-based adaptive critic control of induction motors

This paper presents a new adaptive critic controller to achieve precise position-tracking performance of induction motors using a radial basis function neural network (RBFNN). The adaptive controller consists of an associative search network (ASN), an adaptive critic network (ACN), a feedback controller and a robust controller. Due to the mechanical parameter drift, unmodelled dynamics, actuator saturation, and external disturbances, the exact model of an induction motor is difficult to be obtained. The ASN, which can approximate nonlinear functions, is employed to develop an RBFNN-based feedback control law to deal with the unknown dynamics. The ACN receives a reward from credit-assignment unit to generate an internal reinforcement signal to tune the ASN. Due to the inevitable approximation errors and uncertainties, a robust control technique is developed to reject the effects of the uncertainties. Moreover, the weight updating laws with projection algorithm can tune all parameters of the RBFNN and ensure the localized learning capability. By Lyapunov theory, the stability of the closed-loop system can be guaranteed. In addition, the effectiveness of the proposed RBFNN-based induction motor controller is verified by experimental results.