Parameter-Adaptive Recursive Least Squares Identification and Optimization for Permanent Magnet Synchronous Motors

Accurate parameter identification is fundamental for ensuring high-performance control of motors. The parameters of Permanent Magnet Synchronous Motors (PMSMs) vary during operation and are challenging to measure directly, necessitating online identification. Traditional Recursive Least Squares (RLS) methods exhibit lower dynamic performance, significant errors, substantial perturbations during identification, and high sensitivity to noise when identifying PMSM parameters. This paper investigates the parameter identification problem of PMSMs and proposes an improved method based on RLS. By dynamically adjusting the forgetting factor, smoothing the recognition error and matrix paradigm control. The improved algorithm shows stronger anti-interference ability, higher recognition accuracy and better dynamic performance, making it more reliable and efficient in practical applications.