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| Sensorless Control of IPMSM Based on Improved Discrete Second-Order Sliding Mode Observer |
| Wang Chenchen1, Gou Lifeng1,2, Zhou Minglei1, You Xiaojie1, Dong Shifan1 |
1. School of Electrical Engineering Beijing Jiaotong University Beijing 100044 China;
2. Institute of Science and Technology of China Three Gorges Corporation Beijing 100038 China |
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Abstract The model-based methods for the sensorless control of interior permanent magnet synchronous motor (IPMSM) are used for medium- and high-speed regions and can achieve satisfactory performance above 10% of the rated speed. Among the model-based methods, the sliding mode observer (SMO) has received increasing attention due to its great robustness to system uncertainty. However, the chattering phenomenon of the conventional first-order SMO seriously affect the performance of system control. A super-twisting algorithm based second-order sliding-mode observer (STA-SMO) has been proposed to alleviate chattering. However, the ideal control performance of STA-SMO cannot be achieved since the control frequency is limited in practice, and the STA-SMO exists the compromise between alleviating chattering and the estimation accuracy under the limited control frequency. To address the issue, an improved STA-SMO with a second order general integrator (SOGI) for sensorless control of an IPMSM is proposed in this paper. Furthermore, a discrete-time model of IPMSM based on the extended electromotive force (EEMF) is proposed to remove the cross-coupling effect for the EEMF estimation and improve the estimation accuracy.
In practice, the sensorless control is implemented by a digital controller, so the designed STA-SMO in the continue-time domain must be discretized. In the process of continue-time model discretization, there will be discretization errors and the cross-coupling effect on the estimated EEMF, which will affect the estimation accuracy of the EEMF finally. In this paper, based on the latched model of the stator voltage in the stationary frame and the latched model of the EEMF in the rotating frame, a discrete-time EEMF model is constructed by using a method for developing cross-coupled discrete-time model. Since the estimation accuracy of the rotor position and speed depends on the estimation accuracy of the EEMF, an improved STA-SMO is proposed to estimate the EEMF accurately. Based on the internal model principle, a SOGI is introduced as the internal model of the EEMF to alleviate chattering and improve the estimation accuracy. Different from the conventional STA-SMO which estimates the EEMF directly, the error between the actual and estimated EEMF can be regarded as a state variable in the proposed STA-SMO and estimated firstly, and then the EEMF error signal is used as the input of the SOGI, thus the EEMF can be estimated through the SOGI finally. In order to analyze the stability of the proposed STA-SMO with SOGI using the linear control theory, the transfer function between the actual and the estimated EEMF can be obtained by simplifying the super-twisting algorithm as a linear gain. The frequency response of the transfer function indicates that the magnitude is zero dB and the phase is zero degree at the estimated fundamental frequency. Therefore, not only the EEMF can be estimated accurately, but also the chattering can be alleviated due to the filter characteristic for the high-frequency harmonic. Moreover, the stability of the proposed STA-SMO is proved by Lyapunov approaches. Finally, based on the constructed discrete-time EEMF model, a discrete improved STA-SMO is proposed. In order to verify the effectiveness of the proposed sensorless control, the experiments are carried out on a 3kW IPMSM traction drive system. The EEMF estimation results of the conventional and proposed STA-SMO are compared. The error between the actual and estimated rotor position exceeds 10 degrees using the conventional STA-SMO. Using the proposed STA-SMO, the estimated rotor position and speed can track the actual values well and the chattering can also be well alleviated. The proposed sensorless control is also verified in the speed and load variation condition.
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Received: 14 September 2021
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2023, Vol. 38 
