This paper proposes a position estimation method for a planar switched reluctance motor (PSRM). In the method, a second-order sliding mode observer (SMO) is used to achieve sensorless control of a PSRM for the first time. A sensorless closed-loop control strategy based on the SMO without a position sensor for the PSRM is constructed. The SMO mainly consists of a flux linkage estimation, an adaptive current estimation, an observing error calculation, and a position estimation section. An adaptive current observer is applied in the current estimation section to minimize the error between the measured and estimated currents and to increase the accuracy of the position estimation. The flux linkage is estimated by the voltage equation of the PSRM, and the estimated flux linkage is then used to estimate the phase current in the adaptive current observer. To calculate the observing error of the SMO using the measured and estimated phase currents, the observing error of the thrust force is introduced to replace the immeasurable state error of the position and speed of the mover. The sliding surface is designed based on the error of the thrust force, and stability analysis is given. Once the sliding surface is reached, the mover position is then estimated accurately. Finally, the effectiveness of the proposed method for the PSRM is verified experimentally.
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This work was supported in part by the National Natural Science Foundation of China under Grant NSFC 51677120 and NSFC U1813212, in part by the Natural Science Foundation of Guangdong Province, China, under Grant 2017A030310460 and Grant 2018A030310522, in part by the Shenzhen Government Fund under Grant 20170919104246276 and Grant JCYJ20180305124348603, and in part by the Fundamental Research Funds for the Shenzhen University under Grant 2017039.
Sun, Jundi; Cao, Guang-Zhong; Huang, Su-Dan; Peng, Yeping; He, Jiangbiao; and Qian, Qing-Quan, "Sliding-Mode-Observer-Based Position Estimation for Sensorless Control of the Planar Switched Reluctance Motor" (2019). Electrical and Computer Engineering Faculty Publications. 35.