Reduced-Order Extended Kalman Filter MATLAB Algorithm for Permanent Magnet Synchronous Motors

This paper presents a MATLAB algorithm implementation of a reduced-order extended Kalman filter (EKF) specifically designed for permanent magnet synchronous motors (PMSM). This advanced filtering technique enables precise PMSM control across various operating conditions. The algorithm combines the advantages of both standard Kalman filtering and extended Kalman filtering while enhancing computational efficiency through dimensionality reduction. The implementation typically involves state-space modeling of PMSM dynamics, Jacobian matrix calculations for linearization, and recursive covariance updates. Key MATLAB functions employed may include matrix operations for state prediction, measurement updates, and noise covariance handling. Although optimized for PMSM applications, this algorithm framework can be adapted for other motor types such as induction motors and DC motors through appropriate model modifications.

In practical engineering applications, this algorithm assists engineers in achieving more efficient and accurate PMSM control operations. The MATLAB code structure allows for straightforward integration with other control algorithms like field-oriented control or model predictive control to optimize overall motor performance. Implementation considerations include proper tuning of process and measurement noise matrices, rotor position/speed estimation accuracy, and real-time computation requirements. For engineers and researchers working on PMSM control systems, this algorithm provides a valuable tool for advanced state estimation and system optimization.