Abstract：In finite control set-model predictive flux control (FCS-MPFC) for permanent magnet synchronous machine (PMSM), the parameters are required for flux linkage reference calculation, actual flux estimation, and flux prediction in the following one or two sampling periods. Due to the influence of operation states and temperature, machine parameters are ready to deviate. Therefore, identifying their values online is crucial to the performance of the actual predictive control system, including the d- and q-axis inductances and the permanent magnet flux. This paper proposes a multi-parameter identification scheme for PMSM. Consequently, with the identified parameters, the flux is treated, and an FCS-MPFC scheme is implemented. Firstly, this paper designs a generalized proportional integral observer (GPIO) to observe the disturbance caused by parameter deviations. The motor parameter deviations in the disturbance can be extracted based on the mathematical model of the motor. Then, the q-axis inductance and permanent magnet flux can be accurately identified by compensating the nominal parameters. Next, a d-axis inductance identification method is proposed based on the stator current variation characteristics under the FCS-MPFC strategy, which constructs an identification equation for the d-axis inductance using the d-axis current difference equation. It improves identification accuracy by setting appropriate boundary conditions for the differential current values, overcoming the rank deficiency problem in multi-parameter identification. In addition, boundary conditions are set for current and speed to ensure the identification effectiveness. The low-pass filters eliminate high harmonic noises in the identified motor parameters. Furthermore, the identification process is improved by leveraging the characteristics of inverter voltage output under the FCS-MPFC strategy. Specifically, two current samples are taken within a control cycle. By appropriately setting the sampling moments, the dead zone voltage is eliminated from the identification equation. Finally, for the flux prediction process, a flux prediction model based on GPIO is designed to enhance the robustness of flux prediction parameters. A test platform for PMSM is constructed. Multi-parameter identification, electromagnetic torque increment experiment, and motor acceleration-deceleration experiment are conducted separately. The experimental results demonstrate that the proposed approach can address the rank deficiency issue in multi-parameter identification and eliminate the influence of the inverter dead zone on parameter identification and predictive control. The d-axis inductance, q-axis inductance, and permanent magnet flux under different operating conditions can be identified accurately. Compared to the traditional predictive flux control using nominal parameters, this method eliminates the impact of parameter deviations on torque control. It enables accurate torque control even in the presence of parameter deviations. Furthermore, it outperforms the traditional approach regarding torque control performance under various operating conditions.
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