access icon free Position measurement of linear motor mover based on image spectrum energy

© The Institution of Engineering and Technology
Received 28/06/2018, Accepted 21/01/2019, Revised 03/10/2018, Published 28/01/2019

When the image position measuring method is applied to mover position measurement of a linear motor, the selection of the target source image is crucial to measurement accuracy and anti-interference capacity. In this study, the fence image is used as the original shooting surface, and different kinds of fence images are constructed according to their width standard deviation and grey gradient sum. First, the relationship between the fence image spectrum energy and the correlation of image pixels is analysed. Then the image spectrum energy is used to characterise the feature of fence images during screening to select an aperiodic fence image with a strong anti-interference. Second, the phase singularity method is used to match the two images before and after displacement to obtain a sub-pixel displacement value of two images. Finally, according to the system calibration coefficient, the actual displacement value of the linear motor mover is obtained. Experimental results show that the fence image screening from image spectrum energy exhibits more anti-interference for the position measurement of the linear motor, and the measurement algorithm based on the phase singularity algorithm is more stable. The absolute error of displacement is <0.023 mm, and the relative error change within 0.054–0.862%.

Inspec keywords: displacement measurement; position measurement; gradient methods; angular measurement; image matching; linear motors; calibration

Other keywords: grey gradient sum; image matching; aperiodic fence image; image position; system calibration coefficient; fence image spectrum energy; linear motor mover; fence image screening; image pixels; phase singularity; image spectrum energy; position measurement

Subjects: Image recognition; Spatial variables measurement; Linear machines; Optimisation techniques

References

    1. 1)
      • 24. Xu, Q., Feng, Y., Jia, J.: ‘Research on moisture content prediction by short-time energy spectrum’. Proc. Int. Conf. on Ground Penetrating Radar, Hong Kong, China, 2016, pp. 1–4.
    2. 2)
      • 27. Tomii, N., Yamazaki, M., Arafune, T., et al: ‘Detection algorithm of phase singularity using phase variance analysis for epicardial optical mapping data’, IEEE Trans. Biomed. Eng., 2015, 63, (9), pp. 1795–1803.
    3. 3)
      • 22. Chew, K.S., Zarrabi, K.: ‘Non-contact displacements measurement using an improved particle swarm optimization based digital speckle correlation method’. Proc. Int. Conf. on Pattern Analysis and Intelligence, Putrajaya, Malaysia, 2011, pp. 53–58.
    4. 4)
      • 18. Chung, S.U., Kim, J.M.: ‘Double-sided iron-core PMLSM mover teeth arrangement design for reduction of detent force and speed ripple’, IEEE Trans. Ind. Electron., 2016, 63, (5), pp. 3000–3008.
    5. 5)
      • 31. Zhang, L., Wang, T., Jiang, Z.: ‘High accuracy digital image correlation powered by GPU-based parallel computing’, Opt. Lasers Eng., 2015, 69, pp. 7–12.
    6. 6)
      • 2. Zhu, Y.W., Jin, S.M., Chung, K.S., et al: ‘Control-based reduction of detent force for permanent magnet linear synchronous motor’, IEEE Trans. Magn., 2009, 45, (6), pp. 2827–2830.
    7. 7)
      • 10. Werneck, M.M., Ribeiro, B.A.: ‘Calibration and operation of a fibre Bragg grating temperature sensing system in a grid-connected hydrogenerator’, IET Sci. Meas. Technol., 2013, 7, (1), pp. 59–68.
    8. 8)
      • 5. Lin, F.J., Teng, L.T., Chu, H.: ‘Modified Elman neural network controller with improved particle swarm optimisation for linear synchronous motor drive’, IET Electr. Power Appl., 2010, 2, (3), pp. 201–214.
    9. 9)
      • 6. Du, S., Hu, J., Zhu, Y., et al: ‘An improved displacement measurement based on model reconstruction for permanent magnet synchronous motor’, IEEE Trans. Instrum. Meas., 2017, 66, (11), pp. 3044–3051.
    10. 10)
      • 3. Boldea, I., Tutelea, L., Xu, W., et al: ‘Linear electric machines, drives and MAGLEVs: an overview’, IEEE Trans. Ind. Electron., 2018, 65, (9), pp. 7504–7515.
    11. 11)
      • 17. Wang, H., Zhao, J., Zhao, J., et al: ‘Position detection method of linear motor mover based on extended phase correlation algorithm’, IET Sci. Meas. Technol., 2017, 11, (7), pp. 921–928.
    12. 12)
      • 11. Chen, Z., Pu, H., Liu, X., et al: ‘A time-grating sensor for displacement measurement with long range and nanometer accuracy’, IEEE Trans. Instrum. Meas., 2015, 64, (11), pp. 3105–3115.
    13. 13)
      • 21. Zhao, Y., Pu, R., Bell, S.S., et al: ‘Hyperion image optimization in coastal waters’, IEEE Trans. Geosci. Remote, 2013, 51, (2), pp. 1025–1036.
    14. 14)
      • 30. Ming, L.Y.: ‘Study on the technology of quadric surface extracting base on least square method’. Int. Proc. Int. Conf. on Mechanic Automation and Control Engineering, Inner Mongolia, China, 2011, pp. 5328–5331.
    15. 15)
      • 1. Sato, K., Katori, M., Shimokohbe, A.: ‘Ultrahigh-acceleration moving-permanent-magnet linear synchronous motor with a long working range’, IEEE/ASME Trans. Mech., 2013, 18, (1), pp. 307–315.
    16. 16)
      • 26. Shinriki, M., Takase, H., Sato, R., et al: ‘Multi-range-resolution radar using sideband spectrum energy’, IEE Proc., Radar Sonar Navig., 2006, 153, (5), pp. 396–402.
    17. 17)
      • 23. Zhang, K., Dong, F., Zhao, J.W., et al: ‘Precise measurement of linear motor mover position using an improved digital image correlation based image entropy’, Chin. J. Sci. Instrum., 2015, 36, (12), pp. 2821–2827.
    18. 18)
      • 19. Wang, M., Li, L., Pan, D.: ‘Detent force compensation for PMLSM systems based on structural design and control method combination’, IEEE Trans. Ind. Electron., 2015, 62, (11), pp. 6845–6854.
    19. 19)
      • 4. Hama, T., Sato, K.: ‘High-speed and high-precision tracking control of ultrahigh-acceleration moving-permanent-magnet linear synchronous motor’, Precis. Eng., 2015, 40, pp. 151–159.
    20. 20)
      • 8. Cao, R., Cheng, M., Zhang, B.: ‘Speed control of complementary and modular linear flux-switching permanent-magnet motor’, IEEE Trans. Ind. Electron., 2015, 62, (7), pp. 4056–4064.
    21. 21)
      • 25. Nill, A.N.B., Bouzas, B.: ‘Objective image quality measure derived from digital image power spectra’, Opt. Eng., 1992, 31, (4), pp. 813–825.
    22. 22)
      • 15. Lavatelli, A., Zappa, E.A.: ‘Displacement uncertainty model for 2-D DIC measurement under motion blur conditions’, IEEE Trans. Instrum. Meas., 2017, 66, (3), pp. 451–459.
    23. 23)
      • 29. Yamazato, T., Haruyama, S.: ‘Image sensor based visible light communication and its application to pose, position, and range estimations’, IEICE Trans. Commun., 2014, 97, (9), pp. 1759–1765.
    24. 24)
      • 28. Qiao, Y., Wang, W., Minematsu, N., et al: ‘A theory of phase singularities for image representation and its applications to object tracking and image matching’, IEEE Trans. Image Process., 2009, 18, (10), pp. 2153–2166.
    25. 25)
      • 7. Qiu, L., Shi, Y., Pan, J., et al: ‘Robust cooperative positioning control of composite nested linear switched reluctance machines with network-induced time delays’, IEEE Trans. Ind. Electron., 2018, 65, (9), pp. 7447–7457.
    26. 26)
      • 14. Zhang, L., Gao, Y., Xia, Y., et al: ‘A fine-grained image categorization system by cellet-encoded spatial pyramid modeling’, IEEE Trans. Ind. Electron., 2015, 62, (1), pp. 564–571.
    27. 27)
      • 20. Wang, H., Zhao, J., Zhao, J., et al: ‘A new rapid-precision position measurement method for linear motor mover based on 1-D EPCA’, IEEE Trans. Ind. Electron., 2018, 65, (9), pp. 7485–7494.
    28. 28)
      • 9. Di Rienzo, L., Zhang, Z.: ‘Spatial harmonic expansion for use with magnetic sensor arrays’, IEEE Trans. Magn., 2010, 46, (1), pp. 53–58.
    29. 29)
      • 16. Pham, M.H.: ‘Detecting the optimal active contour in the computed tomography image by using entropy to choose coefficients in energy equation’. Proc. Int. Conf. on Signals and Image Processing, London, 2015, pp. 41–44.
    30. 30)
      • 13. Cheng, P., Menq, C.H.: ‘Real-time continuous image registration enabling ultraprecise 2-D motion tracking’, IEEE Trans. Image Process., 2013, 22, (5), pp. 2081–2090.
    31. 31)
      • 12. Marignetti, F., de Santis, E., Avino, S., et al: ‘Fiber Bragg grating sensor for electric field measurement in the end windings of high-voltage electric machines’, IEEE Trans. Ind. Electron., 2016, 63, (5), pp. 2796–2802.
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