A Computationally Efficient Sequential RLS Estimator for PMSM Parameters: Algorithm Design and Experimental Verification
Abstract
Accurate knowledge of electrical parameters is crucial for high-performance field oriented control (FOC) of permanent magnet synchronous motors (PMSMs). However, parameters vary significantly due to thermal effects and magnetic saturation. This study proposes a computationally efficient sequential recursive least squares (SRLS) algorithm for real-time parameter identification. Unlike conventional methods, proposed approach decouples the d- and q-axis dynamics to reduce the matrix operation complexity. Identification is achieved under persistent excitation provided by the active torque current (i_q) and the standard FOC scheme at a 20 kHz sampling frequency. Experimental validation is conducted on a 500 W motor operating within a speed range of 200–1000 RPM under constant load conditions at room temperature. Results show that the SRLS algorithm achieves a relative estimation error of 2.59 % for resistance and less than 5% for inductance. The proposed algorithm executes in only 26.5µs on an STM32F427 microcontroller, resulting in a 53% CPU load, which represents a 43% reduction in computational burden compared to the standard RLS method.
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References
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Details
Primary Language
English
Subjects
Power Electronics
Journal Section
Research Article
Publication Date
March 31, 2026
Submission Date
February 17, 2026
Acceptance Date
March 24, 2026
Published in Issue
Year 2026 Volume: 13 Number: 1