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Gelişmiş Uyarlanabilir Pencereleme Tekniği ile bir Krank-Biyel Mekanizmasının Gerçek Zamanlı İvme Tahminlemesi

Year 2021, Issue: 28, 506 - 510, 30.11.2021
https://doi.org/10.31590/ejosat.1008233

Abstract

Bu çalışmada, bir krank-biyel mekanizması için ivme tahmini için ikinci dereceden bir uyarlanabilir pencereleme tekniğinin gerçek zamanlı bir uygulaması sunulmaktadır. Doğru bir ivme tahmini için uygun değerlere dikkatlice ayarlanması gereken bazı kritik parametreler olduğu gösterilmiştir. Bu teknikte pencerenin boyutu, konum hataları kullanıcı tarafından tanımlanan hata bantlarının dışına çıkmayıncaya kadar otomatik olarak büyütülür. İkinci mertebeden uyarlamalı pencereleme tekniğinin etkinliğini gerçek zamanlı olarak gösterebilmek için krank-biyel mekanizmasından oluşan bir test düzeneği kullanılmıştır. Yapılan testlerde, devir başına 1024 darbeye sahip artımlı bir optik kodlayıcı ile krank açısı ölçülmekte olup sonrasında ikinci dereceden uyarlamalı pencereleme tekniği kullanılarak bazı kinematik hesaplamalar yardımıyla kızak uzvunun ivmesi tahmin edilmektedir. Tahmin edilen ivme değerleri, kızağa bağlı 0-200 Hz nominal bant genişliğine ve ±6g ölçüm aralığına sahip bir ivmelenme sensörü ile doğrudan karşılaştırılmaktadır. Tüm hareket profili boyunca ivme hatalarının karekök-ortalama değerinin tolere edilebilir düzeyde olduğu ve dolayısıyla bu etkin ivme tahminleme tekniğinin çeşitli çevrimiçi robotik uygulamalarda bir yumuşak sensor olarak kolaylıkla uygulanabileceği gösterilmiştir.

References

  • Jeon, S. and Tomizuka, M. (2007). Benefits of acceleration measurement in velocity estimation and motion control. Control Engineering Practice, 15(3), 325-332.
  • Belanger, P. R., Dobrovolny, P., Helmy, A. and Zhang, X. (1998). Estimation of angular velocity and acceleration from shaft-encoder measurements. Int. J. Robot. Res., 17(11), 1225–1233.
  • Lorenz, R.D. and Van Pattern, K.W. (1991). High-Resolution Velocity Estimation for All-Digital ac Servo Drives. IEEE Transactions on Industry Applications, 27(4), 701-705.
  • Brunsbach, B.J., Henneberger, G. and Klepsch, T. (1992). Speed Estimation with Digital Position Sensor. Proceedings of the International Conference on Electrical Machines (ICEM’92), p. 577-581.
  • Gao, X.Z. and Ovaska, S.J. (2001). Acceleration signal estimation using neural networks. Meas. Sci. Technol., 12(10), 1611–1619.
  • Tanaka, H., Nishi, H. and Ohnishi, K. (2008) .An Approach to Acceleration Estimation Using FPGA. IEEE International Symposium on Industrial Electronics, p. 1959-1964.
  • Janabi-Sharifi, F., Hayward, V., and Chen, C.J. (2000). Discrete-Time Adaptive Windowing for Velocity Estimation. IEEE Transactions on Control Systems Technology, 8(6), 1003-1009.
  • [8] Kilic, E., Baser, O., Dolen, M. and Konukseven, E.I. (2010). An Enhanced Adaptive Windowing Technique for Velocity and Acceleration Estimation using Incremental Position Encoders. The International Conference on Signals and Electronic Systems (ICSES’10), p. 61-64.
  • Jin, J. and Pang, Q. (2014). A novel accelaration estimation algorithm based on Kalman filter and adaptive windowing using low-resolution optical encoder. IEEE International Conference on Control Science and Systems Engineering, p. 185-189.
  • Jin, J. and Huang, S. (2014). A novel accelaration estimation algorithm for mechanical vibration suppression of two-mass system. 17th International Conference on Electrical Machines and Systems, p. 2066-2070.
  • Soylemez, E. (2009). Mechanisms, 4th ed., Ankara, Turkey: Middle East Technical University.

Real-Time Acceleration Estimation of a Slider Crank Mechanism with an Enhanced Adaptive Windowing Technique

Year 2021, Issue: 28, 506 - 510, 30.11.2021
https://doi.org/10.31590/ejosat.1008233

Abstract

In this paper, a real-time implementation of a second-order adaptive windowing technique for acceleration estimation is presented for a slider crank mechanism. It is shown that there are some critical parameters which should be carefully set to proper values for a satisfactory acceleration estimation. In this technique, the size of the window is automatically enlarged until the position errors will not be out of the error bands defined by the user. In order to show the effectiveness of the second-order adaptive windowing technique in real-time, a test setup composed of a slider-crank mechanism is used. In the performed tests, only the crank angle is measured via an incremental encoder having a 1024 pulse per revolution, and then, the slider acceleration is estimated by the help of some kinematic calculations using the outputs of the second-order adaptive windowing technique. The estimated acceleration results are directly compared with an acceleration sensor having a measurement range with ±6g with a nominal 0-200 Hz bandwidth attached on the slider. It is found that the root-mean-square value of the acceleration errors along the entire motion profile is at tolerable level so that this effective acceleration estimation technique could be applied to various on-line robotic applications as a soft sensor.

References

  • Jeon, S. and Tomizuka, M. (2007). Benefits of acceleration measurement in velocity estimation and motion control. Control Engineering Practice, 15(3), 325-332.
  • Belanger, P. R., Dobrovolny, P., Helmy, A. and Zhang, X. (1998). Estimation of angular velocity and acceleration from shaft-encoder measurements. Int. J. Robot. Res., 17(11), 1225–1233.
  • Lorenz, R.D. and Van Pattern, K.W. (1991). High-Resolution Velocity Estimation for All-Digital ac Servo Drives. IEEE Transactions on Industry Applications, 27(4), 701-705.
  • Brunsbach, B.J., Henneberger, G. and Klepsch, T. (1992). Speed Estimation with Digital Position Sensor. Proceedings of the International Conference on Electrical Machines (ICEM’92), p. 577-581.
  • Gao, X.Z. and Ovaska, S.J. (2001). Acceleration signal estimation using neural networks. Meas. Sci. Technol., 12(10), 1611–1619.
  • Tanaka, H., Nishi, H. and Ohnishi, K. (2008) .An Approach to Acceleration Estimation Using FPGA. IEEE International Symposium on Industrial Electronics, p. 1959-1964.
  • Janabi-Sharifi, F., Hayward, V., and Chen, C.J. (2000). Discrete-Time Adaptive Windowing for Velocity Estimation. IEEE Transactions on Control Systems Technology, 8(6), 1003-1009.
  • [8] Kilic, E., Baser, O., Dolen, M. and Konukseven, E.I. (2010). An Enhanced Adaptive Windowing Technique for Velocity and Acceleration Estimation using Incremental Position Encoders. The International Conference on Signals and Electronic Systems (ICSES’10), p. 61-64.
  • Jin, J. and Pang, Q. (2014). A novel accelaration estimation algorithm based on Kalman filter and adaptive windowing using low-resolution optical encoder. IEEE International Conference on Control Science and Systems Engineering, p. 185-189.
  • Jin, J. and Huang, S. (2014). A novel accelaration estimation algorithm for mechanical vibration suppression of two-mass system. 17th International Conference on Electrical Machines and Systems, p. 2066-2070.
  • Soylemez, E. (2009). Mechanisms, 4th ed., Ankara, Turkey: Middle East Technical University.
There are 11 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ergin Kılıç 0000-0002-3099-0303

Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 28

Cite

APA Kılıç, E. (2021). Real-Time Acceleration Estimation of a Slider Crank Mechanism with an Enhanced Adaptive Windowing Technique. Avrupa Bilim Ve Teknoloji Dergisi(28), 506-510. https://doi.org/10.31590/ejosat.1008233