Skip to main content
SpringerLink
Log in
Book cover

Handbook of Model Predictive Control pp 581–605Cite as

Birkhäuser

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

  • Chapter
  • First Online:

Part of the book series: Control Engineering ((CONTRENGIN))

Abstract

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.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Allgöwer, F., Badgwell, T.A., Qin, J.S., Rawlings, J.B., Wright, S.J.: Nonlinear Predictive Control and Moving Horizon Estimation — An Introductory Overview. Springer, London (1999). https://doi.org/10.1007/978-1-4471-0853-5_19

  2. Ariens, D., Houska, B., Ferreau, H., Logist, F.: ACADO: toolkit for automatic control and dynamic optimization. Optimization in Engineering Center (OPTEC), 1.0beta edn. (2010). http://www.acadotoolkit.org/

  3. Bouabdallah, S.: Design and Control of Quadrotors with Application to Autonomous Flying, p. 155. EPFL, Lausanne (2007)

    Google Scholar 

  4. Bouffard, P., Aswani, A., Tomlin, C.: Learning-based model predictive control on a quadrotor: onboard implementation and experimental results. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 279–284 (2012). https://doi.org/10.1109/ICRA.2012.6225035

  5. Chowdhary, G., Mühlegg, M., How, J.P., Holzapfel, F.: Concurrent Learning Adaptive Model Predictive Control, pp. 29–47. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-38253-6_3

  6. Chowdhary, G., Mühlegg, M., How, J.P., Holzapfel, F.: A concurrent learning adaptive-optimal control architecture for nonlinear systems. In: 52nd IEEE Conference on Decision and Control, pp. 868–873 (2013). https://doi.org/10.1109/CDC.2013.6759991

  7. Eren, U., Prach, A., Koçer, B.B., Raković, S.V., Kayacan, E., Açıkmeşe, B.: Model predictive control in aerospace systems: current state and opportunities. J. Guid. Control. Dyn. 40(7), 1541–1566 (2017). https://doi.org/10.2514/1.G002507

    Article  Google Scholar 

  8. Fum, W.Z.: Implementation of simulink controller design on Iris+ quadrotor. Ph.D. thesis, Monterey, California, Naval Postgraduate School (2015)

    Google Scholar 

  9. Garimella, G., Sheckells, M., Kobilarov, M.: Robust obstacle avoidance for aerial platforms using adaptive model predictive control. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5876–5882 (2017). https://doi.org/10.1109/ICRA.2017.7989692

  10. Grúne, L.: NMPC without terminal constraints. IFAC Proc. Vol. 45(17), 1–13 (2012)

    Article  Google Scholar 

  11. Havoutis, I., Calinon, S.: Supervisory teleoperation with online learning and optimal control. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1534–1540 (2017)

    Google Scholar 

  12. Hoffmann, G.M., Huang, H., Waslander, S.L., Tomlin, C.J.: Quadrotor helicopter flight dynamics and control: theory and experiment. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, vol. 2, p. 4 (2007)

    Google Scholar 

  13. Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Distributed nonlinear model predictive control of an autonomous tractor-trailer system. Mechatronics 24(8), 926–933 (2014)

    Article  Google Scholar 

  14. Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Nonlinear modeling and identification of an autonomous tractor-trailer system. Comput. Electron. Agric. 106, 1–10 (2014). https://doi.org/10.1016/j.compag.2014.05.002

    Article  Google Scholar 

  15. Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Learning in centralized nonlinear model predictive control: application to an autonomous tractor-trailer system. IEEE Trans. Control Syst. Technol. 23(1), 197–205 (2015)

    Article  Google Scholar 

  16. Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Robust tube-based decentralized nonlinear model predictive control of an autonomous tractor-trailer system. IEEE/ASME Trans. Mechatron. 20(1), 447–456 (2015)

    Article  Google Scholar 

  17. Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor. Comput. Electron. Agric. 115, 78–87 (2015)

    Article  Google Scholar 

  18. Kayacan, E., Peschel, J.M., Kayacan, E.: Centralized, decentralized and distributed nonlinear model predictive control of a tractor-trailer system: a comparative study. In: 2016 American Control Conference (ACC), pp. 4403–4408 (2016). https://doi.org/10.1109/ACC.2016.7525615

  19. Kraus, T., Ferreau, H., Kayacan, E., Ramon, H., Baerdemaeker, J.D., Diehl, M., Saeys, W.: Moving horizon estimation and nonlinear model predictive control for autonomous agricultural vehicles. Comput. Electron. Agric. 98, 25–33 (2013)

    Article  Google Scholar 

  20. Kühl, P., Diehl, M., Kraus, T., Schlöder, J.P., Bock, H.G.: A real-time algorithm for moving horizon state and parameter estimation. Comput. Chem. Eng. 35(1), 71–83 (2011)

    Article  Google Scholar 

  21. Liu, Y., Rajappa, S., Montenbruck, J.M., Stegagno, P., Bülthoff, H., Allgöwer, F., Zell, A.: Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances. Robot. Auton. Syst. 98, 317–332 (2017). https://doi.org/10.1016/j.robot.2017.08.011

    Article  Google Scholar 

  22. López-Negrete, R., Biegler, L.T.: A moving horizon estimator for processes with multi-rate measurements: a nonlinear programming sensitivity approach. J. Process Control 22(4), 677–688 (2012)

    Article  Google Scholar 

  23. Mehndiratta, M., Kayacan, E.: Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters. Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng. (2017). https://doi.org/10.1177/0954410017703414

  24. Morari, M., Lee, J.H.: Model predictive control: past, present and future. Comput. Chem. Eng. 23(4), 667–682 (1999). https://doi.org/10.1016/S0098-1354(98)00301-9

    Article  Google Scholar 

  25. Nicolao, G.D., Magni, L., Scattolini, R.: Stabilizing receding-horizon control of nonlinear time-varying systems. IEEE Trans. Autom. Control 43(7), 1030–1036 (1998)

    Article  MathSciNet  Google Scholar 

  26. Ostafew, C.J., Schoellig, A.P., Barfoot, T.D.: Robust constrained learning-based NMPC enabling reliable mobile robot path tracking. Int. J. Robot. Res. 35(13), 1547–1563 (2016). https://doi.org/10.1177/0278364916645661

    Article  Google Scholar 

  27. Prach, A., Kayacan, E.: An MPC-based position controller for a tilt-rotor tricopter VTOL UAV. Optim. Control Appl. Methods https://doi.org/10.1002/oca.2350

  28. Rao, C.V., Rawlings, J.B., Mayne, D.Q.: Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE Trans. Autom. Control 48(2), 246–258 (2003). https://doi.org/10.1109/TAC.2002.808470

    Article  MathSciNet  Google Scholar 

  29. Robertson, D.G., Lee, J.H., Rawlings, J.B.: A moving horizon-based approach for least-squares estimation. AIChE J. 42(8), 2209–2224 (1996)

    Article  Google Scholar 

  30. Shin, J., Kim, H.J., Park, S., Kim, Y.: Model predictive flight control using adaptive support vector regression. Neurocomputing 73(4), 1031–1037 (2010). https://doi.org/10.1016/j.neucom.2009.10.002

    Article  Google Scholar 

  31. Vukov, M., Gros, S., Horn, G., Frison, G., Geebelen, K., Jørgensen, J., Swevers, J., Diehl, M.: Real-time nonlinear MPC and MHE for a large-scale mechatronic application. Control Eng. Pract. 45, 64–78 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798

    Mohit Mehndiratta & Siddharth Patel

  2. Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Erkan Kayacan

  3. Aarhus University, Department of Engineering, DK-8000, Aarhus C, Denmark

    Erdal Kayacan

  4. University of Illinois at Urbana–Champaign, Champaign, IL, USA

    Girish Chowdhary

Authors
  1. Mohit Mehndiratta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erkan Kayacan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siddharth Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erdal Kayacan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Girish Chowdhary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erkan Kayacan .

Editor information

Editors and Affiliations

  1. Independent Researcher, London, UK

    Saša V. Raković

  2. Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA

    William S. Levine

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mehndiratta, M., Kayacan, E., Patel, S., Kayacan, E., Chowdhary, G. (2019). Learning-Based Fast Nonlinear Model Predictive Control for Custom-Made 3D Printed Ground and Aerial Robots. In: Raković, S., Levine, W. (eds) Handbook of Model Predictive Control. Control Engineering. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-77489-3_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77489-3_24

  • Published:

  • Publisher Name: Birkhäuser, Cham

  • Print ISBN: 978-3-319-77488-6

  • Online ISBN: 978-3-319-77489-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation