Adaptive Torque Estimation for an IPMSM with Cross-Coupling and Parameter Variations

Abstract

This paper presents a new adaptive torque estimation algorithm for an interior permanent magnet synchronous motor (IPMSM) with parameter variations and cross-coupling between d-and q-axis dynamics. All cross-coupled, time-varying, or uncertain terms that are not part of the nominal flux equations are included in two equivalent mutual inductances, which are described using the equivalent d-and q-axis back electromotive forces (EMFs). The proposed algorithm estimates the equivalent d-and q-axis back EMFs in a recursive and stability-guaranteed manner, in order to compute the equivalent mutual inductances between the d-and q-axes. Then, it provides a more accurate and adaptive torque equation by adding the correction terms obtained from the computed equivalent mutual inductances. Simulations and experiments demonstrate that torque estimation errors are remarkably reduced by capturing and compensating for the inherent cross-coupling effects and parameter variations adaptively, using the proposed algorithm.

Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

“…The minimum rotational speed that can be measured using the speed sensor was found to be 1.31rad/s, or 12.5rpm in the experimental setup. In the experimental arrangement, the PM motor parameters could be different from the nominal values shown in Table I [45,46] and there could be unexpected disturbances and noises.…”

Position and Speed Estimation of Permanent Magnet Machine Sensorless Drive at High Speed Using an Improved Phase-Locked Loop

“…The proposed method reduces the influence of parameter errors by increasing the equivalent damping of the drive system. In addition, [35] developed an adaptive torque estimation for PM machines considering the inductance parameter variation. However, the accuracy of EMF estimation is still sensitive to the parameter variation.…”