Iterative learning-based online calibration of a position sensor system for permanent magnet linear synchronous motors

Permanent magnet linear synchronous motors (PMLSM) are gaining attention in high-precision production systems. Smooth and accurate motion is vital, specifically for applications that involve on-the-move processing. This necessitates highly precise position measurement of the shuttles moving along the curvilinear motor track. In this work, we address an anisotropic magnetoresistive sensor (AMR) based position sensor system, where tolerances in the mounting, the magnetization of the permanent magnets (PM), and the sensor electronics can yield intolerably high position errors if no calibration is performed. This paper proposes a novel, user-friendly, cost-effective online calibration method utilizing iterative learning. The method leverages an acceleration sensor mounted directly on the shuttles, making it readily deployable in final PMLSM setups on production sites. Experimental results from a test stand demonstrate a significant reduction in measurement errors, paving the way for smooth shuttle motion along the entire curvilinear track within the PMLSM.