Optimization-Based Parameter Estimation for PMSMs Under Unified Observable Conditions

Parameter estimation of permanent magnet synchronous motors (PMSMs) is broadly researched, while the parameter observability has not been well-discussed. This article employs the local weak observability theory to obtain unified observable conditions and give a definite reference for parameter estimation. Based on the results, a novel machine parameter estimation algorithm based on convex optimization is proposed to verify these conditions. This scheme establishes an integrated cost function and solves it by the numerical method to estimate parameters. Then, with the proposed approach, several parameters are obtained together by one estimator, which avoids multiple observer designs and reduces the system's complexity. Moreover, convergency analyses of the cost function demonstrate that the states of PMSMs are locally weakly observable at the unified conditions. Finally, the stability of the observable conditions and the feasibility of the proposed method are verified on a 4-kW PMSM experimental setup.