Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved Recursive Least Squares

Permanent magnet synchronous motors have garnered significant attention due to their cost-effectiveness, straightforward design, high power density, and superior control performance. The identification of motor parameters is a prerequisite for certain permanent magnet synchronous motor control methodologies. Consequently, the precision of parameter identification exerts a substantial influence on the control stability and operational efficiency of high-performance permanent magnet synchronous motors. Conventional identification methods are plagued by issues such as data overfitting and convergence to local minima, leading to suboptimal accuracy and poor control performance. To address these challenges, this paper proposes an enhanced recursive least squares algorithm based on a Sliding data window strategy. Simulation results substantiate the efficacy of the proposed method, demonstrating its ability to accurately identify motor parameters during operation, improve identification precision, and reduce errors.