This paper proposes an innovative Supervised model Reference Adaptive System (SuMRAS) for the rotor flux linkage identification in Superficial Permanent Magnet Synchronous Machines (SPMSM). Estimations are updated in an autonomous way when convergence is reached. The proposed SuMRAS can be applied also to operating motors without requiring manual analysis, hence, it is suitable for large-scale implementation in cloud services. The effectiveness of the proposed approach is assessed in a real-world cloud environment through hardware-in-the-loop (HIL) experiments.

Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems.