Publication Cover
Vehicle System Dynamics
International Journal of Vehicle Mechanics and Mobility
Volume 61, 2023 - Issue 5
765
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

Deep reinforcement learning-based antilock braking algorithm

&
Pages 1410-1431 | Received 14 Jul 2021, Accepted 13 May 2022, Published online: 08 Jun 2022

References

  • Rao KN, Kumar RK, Mukhopadhyay R, et al. A study of the relationship between magic formula coefficients and tyre design attributes through finite element analysis. Veh Syst Dyn. 2006;44(1):33–63.
  • Yasui Y, Nitta H, Yoshida T, et al. Experimental approach for evaluating tire characteristics and abs performance. SAE Technical Paper; 2000.
  • Adcox J, Ayalew B, Rhyne T, et al. Interaction of anti-lock braking systems with tire torsional dynamics. Tire Sci Technol. 2012;40(3):171–185.
  • Anderson JR, Adcox J, Ayalew B, et al. Interaction of a slip-based antilock braking system with tire torsional dynamics. Tire Sci Technol. 2015;43(3):182–194.
  • Roth V, Xie K, Kidney J, et al. Investigation of tire longitudinal relaxation length and its effect on dry stopping performance. In: 178th Technical Meeting of the Rubber Division of the American Chemical Society, Inc; 12-14 October 2010, Milwaukee, Wisconsin, USA, Rubber Division - American Chemical Society (ACS).
  • Marshek KM, Cuderman JF, Johnson MJ. Performance of anti-lock braking system equipped passenger vehicles − part iii: Braking as a function of tire inflation pressure. SAE Technical Paper; 2002.
  • Arrigoni S, Cheli F, Gavardi P, et al. Influence of tire parameters on ABS performance. Tire Sci Technol. 2017;45(2):121–143.
  • Sivaramakrishnan S, Singh KB, Lee P. Experimental investigation of the influence of tire design parameters on anti-lock braking system (ABS) performance. SAE Int J Passeng Cars - Mech Syst. 2015;8(2015-01-1511):647–658.
  • Sivaramakrishnan S, Singh KB, Lee P. Influence of tire operating conditions on ABS performance. Tire Sci Technol. 2015;43(3):216–241.
  • Reif K. Brakes, brake control and driver assistance systems. Weisbaden, Germany: Springer Vieweg; 2014.
  • Leiber H, Czinczel A. Four years of experience with 4-wheel antiskid brake systems (ABS). SAE Tech. 1983;Vol 830481:423–430.
  • Day TD, Roberts SG. A simulation model for vehicle braking systems fitted with ABS. SAE Tech. 2002;Vol 2002-01-0559:821–839.
  • Bowman JE, Law E. A feasibility study of an automotive slip control braking system. SAE Technical Paper; 1993.
  • Capra D, Galvagno E, Ondrak V, et al. An ABS control logic based on wheel force measurement. Veh Syst Dyn. 2012;50(12):1779–1796.
  • Gerard M, Pasillas-Lépine W, de Vries E, et al. Improvements to a five-phase ABS algorithm for experimental validation. Veh Syst Dyn. 2012;50(10):1585–1611.
  • Shida Z, WMKYAM , Sakurai R. A study on effects of tire characteristics on stop distance of abs braking with simplified model. In: Proceedings of the 10th International Symposium on Advanced Vehicle Control; Loughborough (UK); 2010.
  • Cabrera JA, Ortiz A, Castillo JJ, et al. A fuzzy logic control for antilock braking system integrated in the imma tire test bench. IEEE Trans Veh Technol. 2005;54(6):1937–1949.
  • Topalov AV, Oniz Y, Kayacan E, et al. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing. 2011;74(11):1883–1893.
  • Poursamad A. Adaptive feedback linearization control of antilock braking systems using neural networks. Mechatronics. 2009;19(5):767–773.
  • John S, Pedro JO. Neural network-based adaptive feedback linearization control of antilock braking system. Int J Artif Intell. 2013;10(S13):21–40.
  • Sardarmehni T, Rahmani H, Menhaj MB. Robust control of wheel slip in anti-lock brake system of automobiles. Nonlinear Dyn. 2014;76(1):125–138.
  • Mirzaei A, Moallem M, Mirzaeian B, et al. Design of an optimal fuzzy controller for antilock braking systems. In: 2005 IEEE Vehicle Power and Propulsion Conference; IEEE; 2005. p. 823–828.
  • Tanelli M, Astolfi A, Savaresi SM. Robust nonlinear output feedback control for brake by wire control systems. Automatica. 2008;44(4):1078–1087.
  • Kayacan E, Oniz Y, Kaynak O. A grey system modeling approach for sliding-mode control of antilock braking system. IEEE Trans Ind Electron. 2009;56(8):3244–3252.
  • Lin CM, Hsu CF. Self-learning fuzzy sliding-mode control for antilock braking systems. IEEE Trans Control Syst Technol. 2003;11(2):273–278.
  • Lin JS, Ting WE. Nonlinear control design of anti-lock braking systems with assistance of active suspension. IET Control Theory Appl. 2007;1(1):343–348.
  • Drakunov S, Ozguner U, Dix P, et al. Abs control using optimum search via sliding modes. IEEE Trans Control Syst Technol. 1995;3(1):79–85.
  • Wu MC, Shih MC. Using the sliding-mode pwm method in an anti-lock braking system. Asian J Control. 2001;3(3):255–261.
  • Wu M, Shih M. Simulated and experimental study of hydraulic anti-lock braking system using sliding-mode pwm control. Mechatronics. 2003;13(4):331–351.
  • Unsal C, Kachroo P. Sliding mode measurement feedback control for antilock braking systems. IEEE Trans Control Syst Technol. 1999;7(2):271–281.
  • Zhang X, Xu Y, Pan M, et al. A vehicle abs adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations. Veh Syst Dyn. 2014;52(4):475–503.
  • Pasillas-Lépine W, Gerard M, Loría A. Design and experimental validation of a nonlinear wheel slip control algorithm. Automatica. 2012;48:1852–1859.
  • Tavernini D, Vacca F, Metzler M, et al. An explicit nonlinear model predictive abs controller for electro-hydraulic braking systems. IEEE Trans Ind Electron. 2019;67(5):3990–4001.
  • Fliess M, Join C. Model-free control. Int J Control. 2013;86(12):2228–2252.
  • Hou Z, Jin S. A novel data-driven control approach for a class of discrete-time nonlinear systems. IEEE Trans Control Syst Technol. 2010;19(6):1549–1558.
  • Spall JC, Cristion JA. Model-free control of nonlinear stochastic systems with discrete-time measurements. IEEE Trans Automat Contr. 1998;43(9):1198–1210.
  • Bertsekas DP. Reinforcement learning and optimal control. Belmont (MA): Athena Scientific; 2019.
  • Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge, MA: MIT press; 2018.
  • Todorov E, Erez T, Tassa YM. A physics engine for model-based control. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems; IEEE; 2012. p. 5026–5033.
  • Gu J, Fang Y, Sheng Z, et al. Double deep q-network with a dual-agent for traffic signal control. Appl Sci. 2020;10(5):1622.
  • Ye F, Cheng X, Wang P, et al. Automated lane change strategy using proximal policy optimization-based deep reinforcement learning. In: 2020 IEEE Intelligent Vehicles Symposium (IV); IEEE; 2020. p. 1746–1752.
  • Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning. Nature. 2015;518(7540):529–533.
  • Mnih V, Badia AP, Mirza M, et al. Asynchronous methods for deep reinforcement learning. In: Proceedings of The 33rd International Conference on Machine Learning, New York, USA, PMLR; 2016. Vol. 48, p. 1928–1937.
  • Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms. arXiv preprint arXiv:170706347. 2017;.
  • Sardarmehni T, Heydari A. Optimal switching in anti-lock brake systems of ground vehicles based on approximate dynamic programming. In: Dynamic Systems and Control Conference; Vol. 57267, Columbus, OH, American Society of Mechanical Engineers; 2015. p. 1–10.
  • Radac MB, Precup RE. Data-driven model-free slip control of anti-lock braking systems using reinforcement q-learning. Neurocomputing. 2018;275:317–329.
  • Krishna Teja Mantripragada V, Krishna Kumar R. Sensitivity analysis of tyre characteristic parameters on abs performance. Veh Syst Dyn. 2020;60(1):47–72.
  • Pacejka H. Tire and vehicle dynamics. 3rd. Oxford: Elsevier; 2005.
  • ISO 21994:2007. Passenger cars – Stopping distance at straight-line braking with ABS – Open-loop test method. International Organization for Standardization. 2007.
  • Clemente AV, Castejón HN, Chandra A. Efficient parallel methods for deep reinforcement learning. arXiv preprint arXiv:170504862. 2017.
  • Espeholt L, Marinier R, Stanczyk P, et al. Seed rl: Scalable and efficient deep-rl with accelerated central inference. arXiv preprint arXiv:191006591. 2019.
  • Espeholt L, Soyer H, Munos R, et al. Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures. In: International Conference on Machine Learning; Stockholm, Sweden, publisher: PMLR; 2018. p. 1407–1416.
  • Stooke A, Abbeel P. Accelerated methods for deep reinforcement learning. arXiv preprint arXiv:180302811. 2018;.
  • Nair A, Srinivasan P, Blackwell S, et al. Massively parallel methods for deep reinforcement learning. arXiv preprint arXiv:150704296. 2015.
  • Plappert M, Houthooft R, Dhariwal P, et al. Parameter space noise for exploration. arXiv preprint arXiv:170601905. 2017.
  • Saltelli A, Tarantola S, Campolongo F, et al. Sensitivity analysis in practice: A guide to assessing scientific models. Chichester: England; 2004.
  • Razavi S, Gupta HV. What do we mean by sensitivity analysis? the need for comprehensive characterization of ‘global’ sensitivity in earth and environmental systems models. Water Resour Res. 2015;51(5):3070–3092.
  • Saltelli A, Annoni P, Azzini I, et al. Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Comput Phys Commun. 2010;181(2):259–270.
  • Bellman R. A markovian decision process. J Math Mech. 1957;6(5):679–684.
  • Williams RJ, Peng J. Function optimization using connectionist reinforcement learning algorithms. Conn Sci. 1991;3(3):241–268.
  • Williams RJ. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn. 1992;8(3):229–256.
  • Schulman J, Moritz P, Levine S, et al. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:150602438. 2015.
  • Schulman J, Levine S, Abbeel P, et al. Trust region policy optimization. In: International conference on machine learning; Lille, France, PMLR; 2015. p. 1889–1897.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.