Neuro-controller for high performance induction motor drives in robots

This paper presents a novel approach to the speed control of an induction motor (IM) as a robust high performance drive (HPD) using on-line self-tuning adapted artificial neural network (ANN). Based on motor dynamics and non-linear unknown load characteristics such as robot systems, a neuro speed controller is developed. The proposed controller is very simple and serves as an identifier and a controller in the same time. The combination of the adaptive learning rate with the epochs used through the on-line training offers a unique feature of system identification and adaptive control. The performance of the controller was evaluated under various operating conditions to track different speed trajectories. The results validate the efficacy of the ANN for the precise tracking control of IM. Furthermore, the use of ANN makes the drive system robust, accurate, and insensitive to parameter variations. Also, the drive system is implemented in real-time using a digital signal processor (DSP) TMS320C31.