On the Comparison of Model-Based and Model-Free Controllers in Guidance, Navigation and Control of Agricultural Vehicles

  • Erkan Kayacan
  • Erdal KayacanEmail author
  • I-Ming Chen
  • Herman Ramon
  • Wouter Saeys
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 362)


In a typical agricultural field operation, an agricultural vehicle must be accurately navigated to achieve an optimal result by covering with minimal overlap during tillage, fertilizing and spraying. To this end, a small scale tractor-trailer system is equipped by using off the shelf sensors and actuators to design a fully autonomous agricultural vehicle. To alleviate the task of the operator and allow him to concentrate on the quality of work performed, various systems were developed for driver assistance and semi-autonomous control. Real-time experiments show that a controller, which gives a satisfactory trajectory tracking performance for a straight line, gives a large steady-state error for a curved line trajectory. On the other hand, if the controller is aggressively tuned to decrease the tracking error for the curved lines, the controller gives oscillatory response for the straight lines. Although existing autonomous agricultural vehicles use conventional controllers, learning control algorithms are required to handle different trajectory types, environmental uncertainties, such as variable crop and soil conditions. Therefore, adaptability is a must rather than a choice in agricultural operations. In terms of complex mechatronics systems, e.g. an agricultural tractor-trailer system, the performance of model-based and model-free control, i.e. nonlinear model predictive control and type-2 neuro-fuzzy control, is compared and contrasted, and eventually some design guidelines are also suggested.


Autonomous Agricultural Vehicles Model-free Controller Nonlinear Model Predictive Controller (NMPC) Complex Mechatronic Systems Tractor-trailer System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Erkan Kayacan
    • 1
  • Erdal Kayacan
    • 2
    Email author
  • I-Ming Chen
    • 2
  • Herman Ramon
    • 3
  • Wouter Saeys
    • 3
  1. 1.Coordinated Science Lab, Distributed Autonomous Systems LabUniversity of Illinois at Urbana -ChampaignUrbanaUSA
  2. 2.School of Mechanical & Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Biosystems, Division of MechatronicsBiostatistics and SensorsLeuvenBelgium

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