Skip to main content

Fuzzy Control Method for Path Tracking System of Combine Harvester

  • Conference paper
  • First Online:
Book cover Artificial Intelligence and Security (ICAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11632))

Included in the following conference series:

Abstract

The high-precision combine harvester path tracking system is a key to protect the efficiency and precision of harvesting. The speed and heading of combine harvester are the main factors influencing the accuracy of tracking, and the speed changes all the time according to the harvester’s status. Traditional pure pursuit algorithm uses the constant look-ahead distance, cannot be adapted to the path tracking conditions of the combine harvester. The fuzzy control method for path tracking system was proposed to tune the look-ahead distance according to the speed and heading. Experiments showed that this method could restrain the maximum error from 0.142 m to 0.059 m, restrain the standard error from 0.042 m to 0.024 m, and improve the accuracy of harvest by 38.4%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bhavya, Y., Venkatesh, B., Thirupathigoud, K.: Mechanization and automation trends in the urban dairy farms: a review. Pharma Innov. J. 7(3), 158–160 (2018)

    Google Scholar 

  2. Han, X.Z., Kim, H.J., Kim, J.Y., et al.: Path-tracking simulation and field tests for an auto-guidance tillage tractor for a paddy field. Comput. Electron. Agric. 112(Sp. Iss. SI), 161–171 (2015)

    Article  Google Scholar 

  3. Rahman, M., Ishii, K.: Heading estimation of robot combine harvesters during turning maneuveres. Sensors 18(5), 1390 (2018)

    Article  Google Scholar 

  4. Hauglin, M., Hansen, E.H., Næsset, E., et al.: Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems. Scand. J. For. Res. 32(8), 1–24 (2017)

    Article  Google Scholar 

  5. Olivera, A., Visser, R.: Development of forest-yield maps generated from Global Navigation Satellite System (GNSS)-enabled harvester StanForD files: preliminary concepts. NZ J. Forest. Sci. 46(1), 3 (2016)

    Google Scholar 

  6. Samuel, M., Hussein, M., Binti, M.: A review of some pure-pursuit based path tracking techniques for control of autonomous vehicle. Int. J. Comput. Appl. 135(1), 35–38 (2016)

    Google Scholar 

  7. Dong, F., Heinemann, W., Kasper, R.: Development of a row guidance system for an autonomous robot for white asparagus harvesting. Comput. Electron. Agric. 79(2), 216–225 (2011)

    Article  Google Scholar 

  8. Li, T., Hu, J., Lei, G., et al.: Agricultural machine path tracking method based on fuzzy adaptive pure pursuit model. Trans. Chin. Soc. Agric. Mach. 44(1), 205–210 (2013)

    Google Scholar 

  9. Zhou, Q., Qiu, Y., Li, L., et al.: Steganography using reversible texture synthesis based on seeded region growing and LSB. CMC Comput. Mat. Continua 55(1), 151–163 (2018)

    Google Scholar 

  10. Hu, J., Gao, L., Bai, X., et al.: Review of research on automatic guidance of agricultural vehicles. Trans. Chin. Soc. Agric. Eng. 31(10), 1–10 (2015)

    Google Scholar 

  11. Scharf, L., Harthill, W., Moose, P.: A comparison of expected flight times for intercept and pure pursuit missiles. IEEE Trans. Aerosp. Electron. Syst. 4, 672–673 (1969)

    Article  Google Scholar 

  12. Elbanhawi, M., Simic, M., Jazar, R.: Receding horizon lateral vehicle control for pure pursuit path tracking. J. Vib. Control 24(3), 619–642 (2018)

    Article  Google Scholar 

  13. Zhu, B.J., Hou, Z.X., Wang, X.Z., et al.: Long endurance and long distance trajectory optimization for engineless UAV by dynamic soaring. CMES Comput. Model. Eng. Sci. 106(5), 357–377 (2015)

    Google Scholar 

  14. Tang, X., Tao, J., Zhiteng, L.I., et al.: Fuzzy control optimization method for stability of path tracking system of automatic transplanter. Trans. Chin. Soc. Agric. Mach. 49(01), 29–34 (2018)

    Google Scholar 

  15. Saleh, A.I., Takieldeen, A., El-Sawi, A.R.: Aerospace vehicle simulation and analysis applying pure pursuit guidance method. Int. J. Comput. Appl. 155(11), 19–21 (2016)

    Google Scholar 

  16. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. Readings Fuzzy Sets Intell. Syst. 15(1), 387–403 (1993)

    Article  Google Scholar 

  17. Zhang, D., Yan, X., Zhang, J., et al.: Use of fuzzy rule-based evidential reasoning approach in the navigational risk assessment of inland waterway transportation systems. Saf. Sci. 82, 352–360 (2016)

    Article  Google Scholar 

  18. Wan, M., Yao, J., Jing, Y., et al.: Event-based anomaly detection for non-public industrial communication protocols in SDN-based control systems. CMC Comput. Mat. Continua 55(3), 447–463 (2018)

    Google Scholar 

Download references

Acknowledgements

The project is supported by the following funds: Primary Research & Development Plan of Jiangsu Province (BE2018384), National Key Research and Development Program (2016YFD0702000), National Natural Science Foundation of China (61773113, 41704025), Natural Science Foundation of Jiangsu Province (No. BK20160668).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li-hui Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qiao, N., Wang, Lh., Zhang, Yx., Xinhua, T. (2019). Fuzzy Control Method for Path Tracking System of Combine Harvester. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24274-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24273-2

  • Online ISBN: 978-3-030-24274-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics