Second-Order Resistance Identification of Permanent Magnet Synchronous Motors Using Kalman Filter

This paper presents a method for second-order resistance identification of permanent magnet synchronous motors (PMSM) using Kalman filter techniques. This topic holds significant importance as PMSM finds extensive applications in modern industrial systems. The implementation typically involves establishing a state-space model where resistance parameters are treated as state variables, with the Kalman filter algorithm recursively estimating these parameters through prediction and correction steps. By employing Kalman filtering, the precision and efficiency of motor control can be substantially improved through real-time parameter tracking. For resistance identification specifically, this method reduces estimation errors and enhances identification accuracy by optimally combining predicted states with noisy measurements. The core algorithm implementation would require defining system matrices (F, H, Q, R) and implementing the recursive Kalman update equations in control software. This research carries substantial implications for motor control and electrical engineering domains, offering improved parameter adaptation capabilities. Furthermore, the methodology holds potential for extension to other fields such as robotics, automation, and aerospace engineering, where similar parameter estimation challenges exist. The implementation could involve embedding the Kalman filter algorithm in DSP or FPGA-based controllers using iterative covariance updates and gain calculations. In summary, this paper provides valuable insights that contribute to advancing PMSM control technology through sophisticated parameter identification techniques.