An intelligent optimization algorithm with novel fitness function for high-performance PMSM FOC

This paper proposes an advanced intelligent optimization algorithm (IO-FOC) with a novel fitness function to improve the dynamic response and steady-state performance of field-oriented control (FOC) systems for permanent magnet synchronous motor (PMSM). The extended state observer (ESO) dynamically compensates for nonlinearities and time-varying parameters, while the IO algorithm optimizes ESO gain and current-loop PI parameters in real time. This dual-layer optimization enhances system robustness, anti-interference capability, and control precision. The novel fitness function, designed using dq-axis current and parameter identification, predicts and compensates for disturbances in inductance, resistance, and magnetic flux linkage, ensuring system stability under complex operating conditions. Experimental validation demonstrates that IO-FOC achieves superior performance in start-up, steady-state, and dynamic conditions compared to conventional methods, highlighting its potential for high-precision industrial applications.