Adaptive Self-Tuning MTPA Vector Controller for IPMSM Drive System

Abstract

This paper presents an adaptive self-tuning maximum torque per ampere (MTPA) vector controller for an interior permanent-magnet synchronous motor (IPMSM) drive system. The control scheme consists of a synchronous frame decoupling current controller, MTPA torque controller, and adaptive parameter estimator. The estimator is applied to the q-axis current dynamics as the d-axis inductance can be assumed to be constant without loss of accuracy. Since the q-axis current dynamics is being disturbed by the magnet's back-EMF voltage, the proposed estimator is combined with a robust active-state decoupling scheme to ensure unbiased parameter estimate. The robust decoupling scheme is realized by estimating the magnet's flux linkage by a simple adaptation algorithm based on the steepest descent method. The system's model is greatly simplified when the robust decoupling scheme is combined with the q-axis current dynamics. Relying on the simplified model, a natural adaptive observer is used to estimate the q-axis current. Unknown motor parameters are estimated by minimizing the state estimation error using an iterative gradient algorithm offered by the affine projection. The estimated parameters are used for the self-tuning control. Experimental results are presented to demonstrate the validity and usefulness of the online parameter estimation and control loop tuning technique