Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

: pp. 168–183
Received: January 04, 2021
Accepted: March 06, 2021

Mathematical Modeling and Computing, Vol. 8, No. 2, pp. 168–183 (2021)

Lviv Polytechnic National University
Lviv Polytechnic National University
University of Alabama at Birmingham
US Army CCDC Ground Vehicle Systems Center, Warren, MI
Lviv Polytechnic National University, Lviv, Ukraine
Lviv Polytechnic National University
Alion Science and Technilogy, Ground Vehicle Systems Center

In this article, a fuzzy controller tuned by reinforcement learning is proposed.  The developed algorithm utilizes a fuzzy logic theory and a reinforcement learning for fine-tuning parameters of the membership function for the fuzzy controller.  Apart from the fuzzy controller developed, a fuzzy corrector of reference input (set-point) signal to the controller is applied.  The fuzzy corrector changes the input (reference) signal of the system and takes into account an original reference input and type of external disturbances. Thus, the designed fuzzy control that is tuned by reinforcement learning is capable to ensure the stable, optimal, and safe performance of the system and takes into account external disturbances.  To verify the performance of the proposed controller, the adaptive fuzzy controller tuned by reinforcement learning is applied to the mathematical model of a wheel locomotion module of an electric vehicle to advance a traction control system.  Therefore, the effectiveness of the proposed adaptive fuzzy controller is proven through the simulation results.

  1. Zhang Y., Li S., Liao L.  Near-optimal control of nonlinear dynamical systems: A brief survey.  Annual Reviews in Control. 47, 71–80 (2019).
  2. Grune L., Pannek J.  Nonlinear Model Predictive Control. Communications and Control Engineering. Springer, Cham (2017).
  3. Bououden S., Chadli M., Karimi H. R.  A Robust Predictive Control Design for Nonlinear Active Suspension Systems.  Asian Journal of Control. 18, 122–132 (2016).
  4. Shin Y. C., Xu C. Intelligent systems: modeling, optimization, and control.  CRC press (2009).
  5. Cervantes J, Yu W., Salazar S., Chairez I.  Takagi–Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems.  IEEE Transactions on Fuzzy Systems. 25 (6), 1601–1615 (2017).
  6. Vantsevich V., Lozynskyy A., Demkiv L., Klos S.  A Foundation for Real-Time Tire Mobility Estimation and Control.  Proc. 19th International and 14th European-African Regional Conference of the ISTVS, Budapest, Hungary (2017).
  7. Dawei M., Yu Z., Meilan Z., Risha N.  Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle.  Computers and Electrical Engineering. 58, 447–464 (2017).
  8. Arabi E., Gruenwald B. C., Yucelen T., Nguyen N.  Intelligent fuzzy energy management research for a uniaxial parallel hybrid electric vehicle.  International Journal of Control. 91 (5), 1195–1208 (2018).
  9. Dorf R. C., Bishop R. H.  Modern control systems.  Pearson (2016).
  10. Behrooz F., Mariun N., Marhaban M. H., Radzi M., Amran M., Ramli A. R.  Review of control techniques for HVAC systems–nonlinearity approaches based on Fuzzy cognitive maps.  Energies. 11 (3), 495 (2018).
  11. Lozynskyy A., Demkiv L.  Application of dynamic systems family for synthesis of fuzzy control with account of non-linearities.  Advances in electrical and electronic engineering. 14 (5), 543–550 (2016).
  12. Demkiv L. I.  Research of dynamic system with unstable subsystem that has one root in the right half-plane.  Mathematical modeling and computing. 1 (2), 156–162 (2014).
  13. Andreev A. F., Kabanau V., Vantsevich V.  Driveline systems of ground vehicles: theory and design.  CRC Press (2010).
  14. Lozynskyy A. O., Demkiv L. I., Vantsevich V. V., Borovets T. V., Gorsich D. J.  An estimation accuracy of state observers under uncertain initial conditions.  Mathematical modeling and computing. 6 (2), 320–332 (2019).
  15. Savitski D., Schleinin D., Ivanov V., Augsburg K., Jimenez E., He R., Barber P.  Improvement of traction performance and off-road mobility for a vehicle with four individual electric motors: driving over icy road.  Journal of Terramechanics. 69, 33–43 (2017).
  16. Osinenko P. V., Geissler M., Herlitzius T.  A method of optimal traction control for farm tractors with feedback of drive torque.  Biosystems engineering. 129, 20–33 (2015).
  17. Kim J., Lee J.  Traction-energy balancing adaptive control with slip optimization for wheeled robots on rough terrain.  Cognitive Systems Research. 49, 142–156 (2018).
  18. Addison A., Vacca A.  Real-Time Parameter Setpoint Optimization for Electro-Hydraulic Traction Control Systems.  Proc. 15th Scandinavian International Conference on Fluid Power, Linköping, Sweden. 144, 104–114 (2017).
  19. Sutton R. S., Barto A. G.  Reinforcement learning: An introduction.  MIT press (2018).
  20. Tay T. T., Mareels I., Moore J. B.  High performance control.  Springer Science and Business Media (2012).
  21. Lozynskyy A., Demkiv L.  Synthesis of multicriteria controller by means of fuzzy logic approach.  Advances in Fuzzy Systems. 2014, Article ID 758207 (2014).
  22. Vantsevich V. V., Lozynskyy A., Demkiv L., Holovach I.  Fuzzy logic control of agile dynamics of a wheel locomotion module.  Dynamics of Vehicles on Roads and Tracks 1: Proc. 25th International Symposium on Dynamics of Vehicles on Roads and Tracks, Rockhampton, Queensland, Australia.  CRC Press (2018).
  23. Chudakov E. A.  Theory of Automobile.  State Publishing House of Machine-Building Literature, Moscow, Russia (1950), (in Russian).
  24. Bekker M. G.  Introduction to Terrain-Vehicle Systems.  Michigan University Ann Arbor (1969).
  25. Kutzbach H. D., Bürger A., Bottinger S.  Rolling radii and moment arm of the wheel load for pneumatic tyres.  Journal of Terramechanics. 82, 13–21 (2019).
  26. Wong J. Y.  Terramechanics and off-road vehicles.  Elsevier (1989).
  27. Gray J. P., Vantsevich V. V., Opeiko A. F., Hudas G. R.  A Method for Unmanned Ground Wheeled Vehicle Mobility Estimation in Stochastic Terrain Conditions.  Proc. 7th Americas Regional Conference of the ISTVS, Tampa, Florida, USA (2013).
  28. Gray J. P., Vantsevich V. V., Overholt J. L.  Indices and Computational Strategy for Unmanned Ground Wheeled Vehicle Mobility Estimation and Enhancement.  Proceedings of the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 6A: 37th Mechanisms and Robotics Conference. Portland, Oregon, USA. August 4–7, 2013.    ASME Paper No. DETC2013-12158 (2014).
  29. Vantsevich V., Gorsich D., Lozynskyy A., Demkiv L., Borovets T.  State Observers for Terrain Mobility Controls: A Technical Analysis.  Uhl T. (eds) Advances in Mechanism and Machine Science, IFToMM WC 2019, Mechanisms and Machine Science. 73, Springer, Cham (2019).