Image Registration Technique to Improve a Self-Sensing Estimation for Model Predictive Controlled Interior PMSM Drives

Current responses based self-sensing control estimates rotor position by detecting magnetic saliency of a machine. However, the estimation error can be resulted from several nonideal factors, especially magnetic cross-saturation, as effects of these factors show up in current responses. This article applies image registration technique to improve the accuracy of a self-sensing estimation for model predictive control (MPC) interior permanent magnet synchronous machines (IPMSM) drives by minimizing impacts of nonideal factors on measured current responses. In commissioning process, two images are generated. The inverter voltage vectors in MPC excite current responses, represented by a set of vectors that generate a response image. Nonideal factors that result in estimation errors also deform the response image. A primitive image based on resolver output can be considered as a template. An image registration technique is investigated to register deformed images with corresponding primitive images, and transformations contain operations of rotation, scale, translation, and shear can be derived. During the drive, the deformed pixel points are online mapped to new pixel points by applying these operations. The current responses after transformation are less subject to nonideal factors. As a result, impacts of these deformation factors that contribute to estimation errors can be reduced. Finally, the application of image registration technique to the self-sensing estimation is verified on a 3.7-kW IPMSM drive.