Learning-Based Fast Nonlinear Model Predictive Control for Custom-Made 3D Printed Ground and Aerial Robots

  • Mohit Mehndiratta
  • Erkan KayacanEmail author
  • Siddharth Patel
  • Erdal Kayacan
  • Girish Chowdhary
Part of the Control Engineering book series (CONTRENGIN)


In this work, our goal is to use an online learning-based nonlinear model predictive control (NMPC) for systems with uncertain and/or time-varying parameters. We have deployed it for two robotic applications in real-time: an agricultural off-road ground vehicle and an aerial robotic system, namely a tilt-rotor tricopter unmanned aerial vehicle. Nonlinear moving horizon estimation (NMHE) is used to estimate the traction parameters in the former and the mass parameter in the latter. Thanks to its learning capability, NMHE makes the proposed framework adaptive – and therefore robust – to time-varying operational conditions. The experimental results for the trajectory tracking problem of the unmanned ground and aerial vehicles demonstrate a robust learning controller that provides an accurate tracking. The experimental results also show that the proposed framework provides a fast and computationally efficient methodology which can easily be implemented in ground and aerial robotic applications with reasonable computation power, where working conditions are time-varying and the modeling of the system is tedious.


Nonlinear Model Predictive Control (NMPC) Aerial Robots Fast NMPC Horizon Estimation Parameter Tractability 
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.



This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme. The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000598.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Mohit Mehndiratta
    • 1
  • Erkan Kayacan
    • 2
    Email author
  • Siddharth Patel
    • 1
  • Erdal Kayacan
    • 3
  • Girish Chowdhary
    • 4
  1. 1.Nanyang Technological University50 Nanyang AvenueSingapore
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Aarhus UniversityDepartment of EngineeringAarhus CDenmark
  4. 4.University of Illinois at Urbana–ChampaignChampaignUSA

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