Embedded position detection theory and error analysis for permanent magnet servo motors
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1. School of Instrumentation Science and Optoelectronics Engineering, Hefei University of Technology, Hefei 230009, China; 2. Engineering Research Center of Mechanical Testing Technology and Equipment, Ministry of Education, Chongqing Key Laboratory of Time Grating Sensing and Advanced Testing Technology, Chongqing University of Technology, Chongqing 400054, China

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TH7

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    Abstract:

    Aiming at the deficiency of high cost and large volume existing in the position sensor of permanent magnet servo motor, as well as the disadvantage of the computation complexity and the unreliability due to relying on the motor parameters existing in the newly developed sensorless technology, the time grating displacement sensor research group proposed a method for detecting the motor rotation position through winding time grating coil. However, because the detection method through winding time grating coil has the shortcomings of complex signal acquisition, poor measurement stability and increasing error due to the uneven coil winding. On the basis of above, this paper proposes a new embedded shaft position detection method for permanent magnet servo motor based on tunnel magnetoresistance (TMR) effect and time grating technology. The position detection principle is analyzed in detail. According to the theoretical deduction for travelling wave expression, the measurement error characteristics caused by the unequal amplitudes of one and two channel standing waves are analyzed, which lays a foundation for the detection structure optimization and further improving the measurement accuracy. At last, the experiment verifies the correctness of the embedded position detection theoretical analysis and the superiority of the proposed detection method. Compared with the detection method through winding time grating coil, the proposed method improves the measurement accuracy nearly by three times and improves the stability by five times.

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  • Received:
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  • Online: November 15,2017
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