Motor parameter identification methods mostly depend on the voltage equations, which leads to the unsatisfactory identification effect at low current and low speed operation. This paper proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor (PMSM) with HF signal injection at both the dq-axes. This method identifies the resistance and the dq-axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis (PSA) based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of PMSM drives, the selection of the amplitude and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2kW motor and confirmed by finite element analysis (FEA). The experimental results show the good identification effect in different operation conditions.

Offline parameter identification of permanent magnet synchronous machines (PMSMs) is of great importance for general-purpose drives with sensorless control. This paper proposes an amplitude-auto-adjusting signal injection (ASI) method for the parameter self-learning of PMSMs at standstill considering inverter nonlinearities and the digital time-delay effect. The ASI method achieves the inductance identification process under various dq-axis currents and at the same time prevents the unexpected rotor rotation during the self-commissioning process. For the test PMSM, the spatial inductance maps of dq-axes and abc-phases concerning the magnetic saturation and cross-coupling effects are identified along with the stator resistance. To enhance the estimation accuracy, an error model of the inverter nonlinearities in dq-axes is established, and a compensation method independent of inverter parameters is proposed based on the Hermite interpolation. In addition, the influence of the digital time-delay effect is analyzed and compensated based on the transient model of the circuits. The effectiveness of the proposed parameter self-learning scheme is confirmed on a 2.2-kW PMSM drive. The accuracy of the experimental results is validated by finite element analysis (FEA) on the test machine.

This paper proposes a new sensorless control scheme of interior permanent magnet synchronous motors (IPMSM) using high-frequency (HF) orthogonal square-wave voltage injection into stationary reference frame (SRF). This scheme is an improvement on the conventional HF rotating sinusoidal voltage injection into SRF. Firstly, discrete sequences of HF currents are applied to establish the whole signal process. Then, a position estimation method without using low-pass filters is proposed and the phase shift caused by high-pass filter is also reduced. Therefore, the proposed sensorless control method would not be adversely affected by digital filters, which can solve the common drawbacks of the current SRF-based injection methods. Furthermore, digital delay in drive systems is considered and its negative influence can be eliminated effectively as well. The sensorless control method is verified by experiments on a 2.2-kW IPMSM drive platform.

In high-frequency (HF) injection based sensorless interior permanent magnet synchronous motor (IPMSM) drives, digital filters and position tracking observers are used for signal processing, but deteriorate the dynamic performance of the drive system. To address this problem, an active disturbance rejection control (ADRC) strategy based sensorless control scheme for IPMSM drive is proposed in this paper. The influence of digital filters and Luenberger observer on the speed-loop bandwidth and the stability are analyzed. A modified position tracking observer based on model prediction method is presented to increase the speed-loop bandwidth. To improve the stiffness performance, an ADRC strategy is proposed by adopting an extended state observer (ESO) and a nonlinear error feedback controller (NEFC), which can estimate and compensate for the total disturbance of the sensorless drive system. The proposed sensorless control scheme was adopted on a sensorless IPMSM drive, and the experimental results confirmed that the expected dynamic performance could be achieved.

High frequency signal injection methods have been widely used in sensorless control of permanent magnet synchronous motor (PMSM) drives. However, the acoustic noise and torque ripples caused by the injected high frequency signal limit the application of the methods. To avoid these drawbacks, this paper proposes a position estimation method that combines derivative calculations of current and zero voltage vector (ZVV) injection, which is especially effective for zero- and low-speed operation of sensorless PMSM drives. This investigation mainly focuses on the extraction of rotor position from ZVV period and the improvements of the accuracy of estimated position. The PMSM equation is developed in the stationary frame when ZVV is applied. The proposed d-axis current allocation scheme ensures the application of the proposed method from no load to rated load. A modified SVPWM control strategy and a ZVV extension scheme are employed to improve the accuracy of estimated position, which also makes the method more realizable. Further, a novel current sampling method that selects two suitable phase currents for derivative calculation according to the voltage sector location is proposed, which can help reduce current derivative ripples and decrease position estimation errors. Finally, the effectiveness of the proposed position estimation strategy is verified on a 1.0 kW interior PMSM (IPMSM) drive platform.

In the high-frequency (HF) signal injection based position sensorless permanent magnet synchronous motor (PMSM) drives, there is an estimated position error caused by the cross-saturation effect and other factors. In order to suppress the position error, an HF injection angle self-adjustment based online suppression method is proposed in this letter. By tracking the sign of the increment of the absolute back electromotive force in the estimated frame, the criterion of the HF injection angle self-adjustment can be determined online and the position error can be mitigated effectively. This method can avoid the offline measurement or the variation of the operating state, which has a satisfactory suppression effect for the position error under different operating conditions. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.