The Effect of ADRC on Vehicle Braking Performance

  • Wenjuan Li
  • Qi ZhangEmail author
  • Yuan Zhang
Original Article


The active disturbance rejection control (ADRC) was proposed as a control strategy for anti-lock braking system (ABS) to improve the braking performance. A real-time simulation model was developed after analyzing the brake force during vehicle braking and the ADRC principle. This experimental real-time simulation platform was developed based on xPC Target. This ABS study was conducted on a dry concrete pavement, and the braking performance indices were analyzed under the ADRC and the bang–bang control. The results suggest that the braking performance is more superior with ADRC than the bang–bang control because the braking time and braking distance were reduced. The xPC Target is a less-costly and practical method for real-time simulation.


Braking performance Active disturbance rejection control Real-time simulation platform Anti-lock braking 



This work was supported by the National Natural Science Foundation of China (51375125) and the China Scholarship Fund (201708230335).


  1. 1.
    Choi SB (2008) Antilock brake system with a continuous wheel slip control to maximize the braking performance and the ride Quality. IEEE Trans Control Syst Technol 16(5):996–1003CrossRefGoogle Scholar
  2. 2.
    Acosta Lúa C, Di Gennaro S, Sánchez Morales ME (2017) Nonlinear adaptive controller applied to an antilock braking system with parameters variations. Int J Control Autom Syst 15(5):2043–2052CrossRefGoogle Scholar
  3. 3.
    Hamersma HA, Schalk Els P (2014) Improving the braking performance of a vehicle with abs and a semi-active suspension system on a rough road. J Terramech 56:91–101CrossRefGoogle Scholar
  4. 4.
    Radac MB, Precup RE (2018) Data-driven model-free slip control of anti-lock braking systems using reinforcement Q-learning. Neurocomputing 275:317–329CrossRefGoogle Scholar
  5. 5.
    Aghasizade S, Mirzaei M (2017) An integrated strategy for vehicle active suspension and anti-lock braking systems. J Theor Appl Vibr Acoust 3(1):98–111Google Scholar
  6. 6.
    Aksjonov A, Vodovozov V, Augsburg K (2018) Design of regenerative anti-lock braking system controller for 4 in-wheel-motor drive electric vehicle with road surface estimation. Int J Autom Technol 19(4):727–742CrossRefGoogle Scholar
  7. 7.
    Mirzaei M, Mirzaeinejad H (2017) Fuzzy scheduled optimal control of integrated vehicle braking and steering systems. IEEE/ASME Trans Mechatron 22(5):2369–2379CrossRefGoogle Scholar
  8. 8.
    Jing LB, Liu L, Qu RH (2017) A novel method of reducing the cogging torque in SPM machine with segmented stator. J Electr Eng Technol 12(2):718–725CrossRefGoogle Scholar
  9. 9.
    Zang HQ, Zhang SY, Dai Y (2017) The research of vehicle handling performance and stability based on the integrated control of electronic stability program and anti-block braking system. Tech Bull 55(8):634–640Google Scholar
  10. 10.
    Sardarmehni T, Rahmani H, Menhaj M (2014) Robust control of wheel slip in anti-lock brake system of automobiles. Nonlinear Dyn 76(1):125–138CrossRefGoogle Scholar
  11. 11.
    Chiang WP, Yin D, Omae M (2014) Integrated slip-based torque control of antilock braking system for in-wheel motor electric vehicle. IEEJ J Ind Appl 3(4):318–327Google Scholar
  12. 12.
    Jeon J, Choi SB, Lee YS (2014) Wheel slip control of vehicle ABS using piezoactuator-based valve system. Adv Mech Eng 6(1):1–13Google Scholar
  13. 13.
    Mirzaeinejad H (2018) Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network. Appl Soft Comput 70:318–329CrossRefGoogle Scholar
  14. 14.
    Wang WY, Chen MC, Su SF (2012) Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle. Automatica 48(8):1698–1706MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Ahmad F, Amri Mazlan S, Hudha K (2018) Fuzzy fractional PID gain controller for antilock braking system using an electronic wedge brake mechanism. Int J Veh Saf 10(2):97–121CrossRefGoogle Scholar
  16. 16.
    Yang FG, Li YB, Ruan JH (2009) ADRC control for four wheel independent-drive electric vehicle TCS. Electr Mach Control 13(5):739–743Google Scholar
  17. 17.
    Yao JY, Deng WX (2017) Active disturbance rejection adaptive control of hydraulic servo systems. IEEE Trans Ind Electron 64(10):8023–8032CrossRefGoogle Scholar
  18. 18.
    Xia YQ, Fu MY, Li CM (2018) Active disturbance rejection control for active suspension system of tracked vehicles with gun. IEEE Trans Ind Electron 65(5):4051–4060CrossRefGoogle Scholar
  19. 19.
    Herrera L, Li C, Yao X (2015) FPGA-based detailed real-time simulation of power converters and electric machines for EV HIL applications. IEEE Trans Ind Appl 51(2):1702–1712CrossRefGoogle Scholar
  20. 20.
    Faruque MO, Strasser T, Lauss G (2015) Real-time simulation technologies for power systems design, testing, and analysis. IEEE Power Energy Technol Syst J 2(2):63–73CrossRefGoogle Scholar
  21. 21.
    Li H, Steurer M, Shi KL (2006) Development of a unified design, test, and research platform for wind energy systems based on hardware-in-the-loop real-time simulation. IEEE Trans Ind Electron 53(4):1144–1151CrossRefGoogle Scholar
  22. 22.
    Jing LB, Luo ZH, Liu L (2016) Optimization design of magnetic gear based on genetic algorithm toolbox of matlab. J Electr Eng Technol 11(5):1202–1209CrossRefGoogle Scholar
  23. 23.
    Huerta F, Tello RL, Prodanovic M (2017) Real-time power-hardware-in-the-loop implementation of variable-speed wind turbines. IEEE Trans Ind Electron 64(3):1893–1904CrossRefGoogle Scholar
  24. 24.
    Wang G, Liu R, Zhao N, Ding D, Xu DG (2019) Enhanced linear ADRC strategy for HF pulse voltage signal injection based sensorless IPMSM drives. IEEE Trans Power Electron 34(1):514–525CrossRefGoogle Scholar
  25. 25.
    Yan RT, Wang P (2018) Active disturbance rejection control for single-phase PWM rectifier with current decoupling control. J Electr Eng Technol 13(6):2354–2363Google Scholar
  26. 26.
    Han JQ (2008) Active disturbance rejection control technique. National Defend Industry Press, BeijingGoogle Scholar
  27. 27.
    Han JQ (2009) From PID to active disturbance rejection control. IEEE Trans Ind Electron 56(3):900–906CrossRefGoogle Scholar
  28. 28.
    Yang D, Li LT, Yang X (2002) System real-time simulation development environment and application. Tsinghua University Press, BeijingGoogle Scholar
  29. 29.
    Shi PL, Miao LD, Zou GD (2009) Development of uniform hardware driver for real-time windows and xPC target. Proceedings of the ICIC, Manchester, pp 377–380Google Scholar
  30. 30.
    The Mathworks Inc (2012) xPC target 4 device drivers.

Copyright information

© The Korean Institute of Electrical Engineers 2020

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringHarbin University of Science and TechnologyHarbinChina
  2. 2.Department of Mechanical and Power EngineeringHarbin University of Science and TechnologyHarbinChina

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