Skip to main content
Springer Nature Link
Log in

An Improved Fuzzy Logic Control Method for Combine Harvester’s Cleaning System

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Impurities rate and losses rate are two main indicators for the performance of a cleaning system in combine harvester. The impurities rate and losses rate are affected by both the external environment factors and mechanical structural parameters of the system. To obtain an ideal cleaning effect, an effective control strategy, which should be able to adjust the key controlled variables based on the real-time working parameters detection, is required. Due to the nonlinear process of grain harvesting, there is no precise mathematical model to describe the behavior of the cleaning system. By means of combining fuzzy logic control (FLC) theory and expert knowledge, this paper presents an improved FLC algorithm for the cleaning system which is mounted in our self-designed combine harvester. A control system is developed to implement the proposed algorithm. Experiments were conducted to evaluate the cleaning performance. The average values of the impurities rate and losses rate in low, middle and high modes based on our fuzzy control method are 1.66 and 1.69%, which are better than the corresponding values of 2.13 and 2.11% from the classical control method. The results show that the established control system is reliable and effective to improve harvest efficiency.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. Liang, Z.W., Li, Y.M., Xu, L.Z., Zhao, Z., and Tang, Z., Optimum design of an array structure for the grain loss sensor to upgrade its resolution for harvesting rice in a combine harvester, Biosyst. Eng., 2017, vol. 157, pp. 24–34. https://doi.org/10.1016/j.biosystemseng.2017.02.006

    Article  Google Scholar 

  2. Ning, X.B., Chen, J., Li, Y.M., Wang, K., Wang, Y., and Wang, X., Kinetic model of combine harvester threshing system and simulation and experiment of speed control, Trans. Chin. Soc. Agric. Eng., 2015, vol. 31, no. 21, pp. 25–34.

    Google Scholar 

  3. Miu, P.I. and Kutzbach, H.D., Modeling and simulation of grain threshing and separation in threshing units—Part I, Comput. Electron. Agric., 2017, vol. 60, no. 1, pp. 96–104.  https://doi.org/10.1016/j.compag.2007.07.003

    Article  Google Scholar 

  4. Craessaerts, G., Saeys, W., Missotten, B., and Baerdemaeker, J., Identification of the cleaning process on combine harvester. Part I: A fuzzy model for prediction of the material other than grain (MOG) content in the grain bin, Biosyst. Eng., 2008, vol. 101, no. 1, pp. 42–49.  https://doi.org/10.1016/j.biosystemseng.2008.05.016

    Article  Google Scholar 

  5. Aenllea, M.L. and Brinckerb, R., Model scaling in operational modal analysis using a finite element model, Int. J. Mech. Sci., 2013, vol. 76, pp. 86–101.  https://doi.org/10.1016/j.ijmecsci.2013.09.003

    Article  Google Scholar 

  6. Liang, Z.W., Xu, L.Z., and Baerdemaeker, D., Optimisation of a multi-duct cleaning device for rice combine harvesters utilizing CFD and experiments, Biosyst. Eng., 2020, vol. 190, pp. 25–40.  https://doi.org/10.1016/j.biosystemseng.2019.11.016

    Article  Google Scholar 

  7. Xu, L.Z., Hansen, A.C., Li, Y.M., and Yu, L.J., Numerical and experimental analysis of airflow in a multi-duct cleaning system for a rice combine harvester, Trans. Am. Soc. Agric. Biol. Eng., 2016, vol. 59, no. 5, pp. 1101–1110.  https://doi.org/10.13031/trans.59.11569

    Article  Google Scholar 

  8. Myhan, R. and Jachimczyk, E., Grain separation in a straw walker unit of a combine harvester: Process model, Biosyst. Eng., 2016, vol. 145, pp. 93–107.  https://doi.org/10.1016/j.biosystemseng.2016.03.003

    Article  Google Scholar 

  9. Gebrehiwot, M.G., De Baerdemaeker, J., and Baelmans, М., Effect of a cross-flow opening on the performance of a centrifugal fan in a combine harvester: Computational and experimental study, Biosyst. Eng., 2010, vol. 105, no. 2, pp. 247–256. https://doi.org/10.1016/j.biosystemseng.2009.11.003

    Article  Google Scholar 

  10. Uhl, J.B. and Lamp, B.J., Pneumatic separation of grain and straw mixtures, Trans. Am. Soc. Agric. Biol. Eng., 1996, vol. 9, no. 2, pp. 244–246.

    Google Scholar 

  11. Farran, L.G. and Macmillan, R.H., Grain-chaff separation in a vertical air stream, J. Agric. Eng. Res., 1979, vol. 24, no. 2, pp. 115–129.  https://doi.org/10.1016/0021-8634(79)90046-5

    Article  Google Scholar 

  12. Craessaerts, G., Saeys, W., Missotten, B., and De Baerdemaeker, J., A genetic input selection methodology for identification of the cleaning process on a combine harvester, Part I: Selection of relevant input variables for identification of the sieve losses, Biosyst. Eng., 2007, vol. 98, no. 2, pp. 166–175. https://doi.org/10.1016/j.biosystemseng.2007.07.008

    Article  Google Scholar 

  13. Benson, E.R., Reid, J.F., and Zhang, Q., Development of an automated combine guidance system, ASAE Ann. Int. Meeting, Milwaukee, Wis., 2000, St. Joseph, Mo.: American Society of Agricultural Engineers, 2000, pp. 1–11.

  14. Wang, Y.F., Wang, D.H., and Chai, T.Y., Modeling and control compensation of nonlinear friction using adaptive fuzzy systems, Mech. Syst. Signal Process., 2009, vol. 23, no. 8, pp. 2445–2457. https://doi.org/10.1016/j.ymssp.2009.05.006

    Article  Google Scholar 

  15. Gundoshmian, T.M., Ghassemzadeh, H.R., Abdollahpour, S., and Navid, H., Application of artificial neural network in prediction of the combine harvester performance, J. Food Agric. Environ., 2010, vol. 8, no. 2, pp. 721–724.

    Google Scholar 

  16. Maertens, K., Ramon, H., and De Baerdemaeker, J., An on-the-go monitoring algorithm for separation processes in combine harvesters, Comput. Electron. Agric., 2004, vol. 43, no. 3, pp. 197–207.  https://doi.org/10.1016/j.compag.2004.01.004

    Article  Google Scholar 

  17. Marathe, G. and Zeuner, M., Development of a new combine harvester for Asian market requirements deployment of tangential-axial flow technology, VDI Berichte, 2007, vol. 2001, p. 367.

    Google Scholar 

  18. Lenaerts, B., Missotten, B., De Baerdemaeker, J., and Saeys, W., LiDaR sensing to monitor straw output quality of a combine harvester, Comput. Electron. Agric., 2012, vol. 85, pp. 40–44.  https://doi.org/10.1016/j.compag.2012.03.011

    Article  Google Scholar 

  19. Liang, Z.W., Li, Y.M., Xu, L.Z., and Zhao, Z., Sensor for monitoring rice grain sieve losses in combine harvesters, Biosyst. Eng., 2016, vol. 147, pp. 51–66. https://doi.org/10.1016/j.biosystemseng.2016.03.008

    Article  Google Scholar 

  20. Craessaerts, G., Baerdemaeker, J.D., Missotten, B., and Sayes, W., Fuzzy control of the cleaning process on a combine harvester, Biosyst. Eng., 2010, vol. 106, no. 2, pp. 103–111.  https://doi.org/10.1016/j.biosystemseng.2009.12.012

    Article  Google Scholar 

  21. Mamdani, E.H. and Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Human-Comput. Stud., 1999, vol. 51, no. 2, pp. 135–147.  https://doi.org/10.1006/ijhc.1973.0303

    Article  MATH  Google Scholar 

  22. Omid, M., Lashgari, M., Mobli, H., Alimardani, R., Mohtasebi, S., and Hesamifard, R., Design of fuzzy logic control system incorporating human expert knowledge for combine harvester, Expert Syst. Appl., 2010, vol. 37, no. 10, pp. 7080–7085.  https://doi.org/10.1016/j.eswa.2010.03.010

    Article  Google Scholar 

  23. Mac, T.T., Copot, C., De Keyser, R., Tran, T.D., and Vu, T., MIMO fuzzy control for autonomous mobile robot, J. Autom. Control Eng., 2016, vol. 4, no. 1, pp. 65–70. https://doi.org/10.12720/joace.4.1.65-70

    Article  Google Scholar 

  24. Castro, J. and Delgado, M., Fuzzy systems with defuzzification are universal approximators, IEEE Trans. Syst., Man Cybern., B, 1996, vol. 26, no. 1, pp. 149–152.  https://doi.org/10.1109/3477.484447

Download references

Funding

This work is finical supported by School Level Talent Fund of Hefei University (20RC12), Anhui Provincial Development and Reform Commission 2020 New Energy Vehicle Industry Innovation Development Project: Key System Research and Vehicle Development for Mass Production Oriented Highly Autonomous Driving (wfgcyh2020477), University Synergy Innovation Program of Anhui Province (GXXT-2019-048), National key research and development plan during the 13th Five-Year Plan period (no. 2016YFD0702002).

Author information

Authors and Affiliations

  1. Advanced Manufacturing Engineering College of Hefei University, Hefei, China

    Wei Li, Gang Lv, Huaichu Dai & Chunpeng Zhang

  2. National Synchrotron Radiation Laboratory University of Science and Technology of China, Hefei, China

    Kai Zhang

Authors
  1. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huaichu Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chunpeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Li.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei Li, Zhang, K., Lv, G. et al. An Improved Fuzzy Logic Control Method for Combine Harvester’s Cleaning System. Aut. Control Comp. Sci. 56, 337–346 (2022). https://doi.org/10.3103/S0146411622040058

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411622040058

Keywords: