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Parameter Identification of Permanent Magnet Synchronous Motor Based on Multi-innovation Least Square Method

On-line accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for achieving high-performance motor control, and it also provides a basis for system fault diagnosis. In order to improve the identification accuracy of the least squares method, speed up the convergence speed of identification, and enhance the robustness of the system, this paper proposes a multi-innovation least squares (MILS) method with forgetting factor. This method expands the single innovation scalar in the identification process to multiple innovation vectors to suppress the influence of interfering data on the identification results, and then introduces a forgetting factor to solve the data saturation problem. The simulation verified that the multiple innovation least squares method with forgetting factor has strong antidisturbance ability and better tracking performance.