DSP Implementation of a Neural Network Vector Controller for IPM Motor Drives

Abstract

This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions.

Development of Lower Computer for Hydraulically Actuated Quadruped Bionic Robots Based on DSP

“…However, this paper uses digital signal processor TMS320F28335 in series of DSP2000 produced by TI (Texas Instruments) Company (Dallas, TX, USA). It combines the high processing ability and the peripheral circuit applied to the hydraulic control in one, greatly reducing the volume of control system and improving the performance of system [5].…”