A novel neural network controller and its efficient DSP implementation for vector-controlled induction motor drives

An artificial neural network controller is experimentally implemented on the Texas Instruments TMS320C30 digital signal processor (DSP). The controller emulates indirect field-oriented control for an induction motor, generating direct and quadrature current command signals in the stationary frame. In this way, the neural network performs the critical functions of slip estimation and matrix rotation internally. There are five input signals to the neural network controller, namely, a shaft speed signal, the synchronous frame present and delayed values of the quadrature axis stator current, as well as two neural network output signals fed back after a delay of one sample period. The proposed three-layer neural network controller contains only 17 neurons in an attempt to minimize computational requirements of the digital signal processor. This allows DSP resources to be used for other control purposes and system functions. For experimental investigation, a sampling period of 1 ms is employed. Operating at 33.3 MHz (16.7 MIPS), the digital signal processor is able to perform all neural network calculations in a total time of only 280 mus or only 4700 machine instructions. Torque pulsations are initially observed, but are reduced by iterative re-training of the neural network using experimental data. The resulting motor speed step response (for several forward and reverse step commands) quickly tracks the expected response, with negligible error under steady-state conditions.