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Control of unmanned agricultural vehicles using neural network-based control system

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Abstract

Recent advances in the fields of robotics, control systems, and wireless communication technologies have accelerated the studies related with unmanned agricultural vehicles. The unmanned agricultural vehicles have been developed to perform various functions in agricultural fields such as seeding, spraying, hoeing, weed and pest control, and harvest. Given the reduction in the number of people working in agriculture and industry, it is obvious that intelligent unmanned agricultural vehicles will be an indispensable part of agriculture in the future. In this paper, PID controller and model-based neural network PID control system has been applied for controlling an unmanned agricultural vehicle. To test the performance of control structures developed, three different input signals were used. When the results of both control structures were examined in the way of transient-state response (such as rise time, settling time, peak time, peak value, settling time) and steady-state response, the control system produced had favorable results.

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References

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

    Article  Google Scholar 

  2. De-An Z, Jidong L, Wei J, Ying Z, Yu C (2011) Design and control of an apple harvesting robot. Biosyst Eng 110:112–122

    Article  Google Scholar 

  3. Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61:63–78

    Article  Google Scholar 

  4. Mousazadeh H (2013) A technical review on navigation systems of agricultural autonomous off-road vehicles. J Terrramech 50:211–232

    Article  Google Scholar 

  5. Mehta SS, Burksa TF, Dixonb WE (2008) Vision-based localization of a wheeled mobile robot for greenhouse applications: a daisy-chaining approach. Comput Electron Agric 63:28–37

    Article  Google Scholar 

  6. Astrand B, Baerveldt A (2005) A vision based row-following system for agricultural field machinery. Mechatronics 15:251–269

    Article  Google Scholar 

  7. Yang L, Noguchi N (2012) Human detection for a robot tractor using omni-directional stereo vision. Comput Electron Agric 89:116–125

    Article  Google Scholar 

  8. Keicher R, Seufert H (2000) Automatic guidance for agricultural vehicles in Europe. Comput Electron Agric 25:169–194

    Article  Google Scholar 

  9. Hiremath SA, Van der Heijden GWAM, Van Evert FK, Stein A, Ter Braak CJF (2014) Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Comput Electron Agric 100:41–50

    Article  Google Scholar 

  10. Xue J, Zhang L, Grift TE (2012) Variable field-of-view machine vision based row guidance of an agricultural robot. Comput Electron Agric 84:85–91

    Article  Google Scholar 

  11. Hiremath S, Van Evert FK, Braak C, Stein A, Van der Heijden G (2014) Image-based particle filtering for navigation in a semi-structured agricultural environment. Biosyst Eng 121:85–95

    Article  Google Scholar 

  12. Toda M, Kitani O, Okamoto T, Torii T (1999) Navigation method for a mobile robot via sonar-based crop row mapping and fuzzy logic control. J Agric Engin Res 72:299–309

    Article  Google Scholar 

  13. Ratner D, McKerrow P (2003) Navigating an outdoor robot along continuous landmarks with ultrasonic sensing. Robotics Auton Syst 45:73–82

    Article  Google Scholar 

  14. Backman J, Oksanen T, Visala A (2012) Navigation system for agricultural machines. Comput Electron Agric 82:32–43

    Article  Google Scholar 

  15. Katsumi M, Katsuyuki T (2009) Navigation control for electric vehicles using nonlinear state feedback H∞ control. Nonlinear Anal 71:2920–2933

    Article  MathSciNet  MATH  Google Scholar 

  16. Munoz JC, Bengochea-Guevara JM, Andujar D, Riberio A (2016) Route planning for agricultural taks: a general approach for fleets of autonomous vehicles in site-specific herbicide applications. Comput Electron Agric 127:204–220

    Article  Google Scholar 

  17. Plessen MMG, Bemporad A (2017) Reference trajectory planning under constraints and path tracking using linear time-varying model predictive control for agricultural machines. Biosyst Eng 153:28–41

    Article  Google Scholar 

  18. Tavoosi V, Kazemi R, Hosseini SM (2014) Vehicle handling improvement with steer-by-wire system using hardware in the loop method. J Appl Res Technol 12:769–781

    Article  Google Scholar 

  19. Farhan AS (2013) Modeling and control solutions for electric vehicles. Eur Sci J 9:222–240

    Google Scholar 

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Authors and Affiliations

  1. Engineering Faculty, Mechatronics Engineering Department, Erciyes University, 38039, Kayseri, Turkey

    İkbal Eski

  2. Agriculture Faculty, Biosystems Engineering Department, Erciyes University, 38039, Kayseri, Turkey

    Zeynel Abidin Kuş

Authors
  1. İkbal Eski
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  2. Zeynel Abidin Kuş
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Correspondence to İkbal Eski.

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The authors declare that they have no conflict of interest.

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Eski, İ., Kuş, Z.A. Control of unmanned agricultural vehicles using neural network-based control system. Neural Comput & Applic 31 (Suppl 1), 583–595 (2019). https://doi.org/10.1007/s00521-017-3026-4

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  • DOI: https://doi.org/10.1007/s00521-017-3026-4

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