基于FPGA采样点自调整算法的SPMSM多参数辨识OA

The adaptive sampling point algorithm based on field programmable gate array(FPGA)was proposed to solve the non-ideal interferences in the multi-parameter identification of surface-mounted permanent magnet synchronous motor(SPMSM)using switching frequency harmonics.The switching frequency harmonics filtered out in the motor control system were introduced into the SPMSM model.The inductance was calculated independently,and the stator resistance and rotor flux linkage were identified step by step.The identification method was not affected by initial parameter values.The signal optimization was implemented on FPGA,and the high-frequency clock signals were used for voltage tracking,while the linear tracking differentiators were adopted to suppress the digital differential noise and reduce the errors caused by inverter nonlinearity.The sampling point self-adjustment rules were designed according to the states of switching modulation signals to improve the adaptability of the algorithm.The experimental results show that the average identification error is less than 1.0％at medium and high speeds,and the maximum error is within 4.5％at low speeds.When the identified parameters are applied to decoupling control under parameter mismatch,the current fluctuation is reduced,and the speed regulation time is shortened,which illuminates that the overall control performance of the system is greatly improved.