Parameter Identification of PMSM Based on Improved Secretary Bird Optimization Algorithm

Permanent magnet synchronous motors (PMSMs) serve as the critical propulsion component in electric aircraft systems, where accurate parameter identification is essential for optimal performance. To address the limitations of conventional methods in balancing precision and computational efficiency, this study develops an improved Secretary Bird Optimization Algorithm (ISBOA) featuring multiple algorithmic enhancements. The proposed approach incorporates Tent chaotic mapping for population initialization, stochastic differential mutation for solution diversity preservation, random interaction transfer for local optima avoidance, and adaptive step-size adjustment for balanced search performance. Successfully implemented on a TMS320F28335 DSP-based control platform, ISBOA demonstrates superior experimental performance with parameter identification errors below 2.6\% and significantly faster convergence compared to traditional methods. This work provides both algorithmic advances in optimization techniques and a practical solution for real-time motor control in electric aviation applications.