Open Access
| Issue |
MATEC Web Conf.
Volume 347, 2021
12th South African Conference on Computational and Applied Mechanics (SACAM2020)
|
|
|---|---|---|
| Article Number | 00018 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/matecconf/202134700018 | |
| Published online | 23 November 2021 | |
- SAE, Antilock Brake System Review, SAE International (2014) [Google Scholar]
- Garrott, W. R. & Mazzae, E. N., An overview of the national highway traffic safety administration’s light vehicle antilock brake systems research program, Technical report SAE Technical Paper (1999) [Google Scholar]
- Hamersma, H. A, ABS braking on rough terrain, PhD thesis, University of Pretoria (2017) [Google Scholar]
- Aly, A. A., Zeidan, E.-S., Hamed, A., Salem, F. et al., ‘An antilock-braking systems (abs) control: A technical review’, Intelligent Control and Automation 2, 186–195 (2011) [CrossRef] [Google Scholar]
- Radac, M.-B. & Precup, R.-E, ‘Data-driven model-free slip control of anti-lock braking systems using reinforcement q-learning’, Neurocomputing 275, 317–329 (2018) [CrossRef] [Google Scholar]
- Zhao, D., Wang, B. & Liu, D, ‘A supervised actor–critic approach for adaptive cruise control’, Soft Computing 17(11), 2089–2099, (2013) [CrossRef] [Google Scholar]
- 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’, American Society of Mechanical Engineers 57267, p. V003T50A010, (2015) [Google Scholar]
- Bosch Gmbh, R. & Girling, P, Bosch Driving-safety systems, Society Of Automotive Engineers (1999) [Google Scholar]
- Canudas-de Wit, C., Tsiotras, P., Velenis, E., Basset, M. & Gissinger, G, ‘Dynamic friction models for road/tire longitudinal interaction’, Vehicle System Dynamics 39(3), 189–226, (2003) [CrossRef] [Google Scholar]
- Penny, W. C. W, ‘Anti-lock braking system performance on rough terrain’, Master’s thesis, University of Pretoria, (2016) [Google Scholar]
- van der Merwe, N. A., Els, P. S. & Žuraulis, V, ‘Abs braking on rough terrain’, Journal of Terramechanics 80, 49–57, (2018) [CrossRef] [Google Scholar]
- Sutton, R. S. & Barto, A. G, Reinforcement learning: An introduction, MIT press, (2018) [Google Scholar]
- Christodoulou, P. Deep-reinforcement-learning-algorithms-with-pytorch, (2017). URL: Available at: https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch [Google Scholar]
- Hochreiter, S. & Schmidhuber, J, ‘Long short-term memory’, Neural computation 9(8), 1735–1780, (1997) [CrossRef] [Google Scholar]
- Bai, S., Kolter, J. Z. & Koltun, V, ‘An empirical evaluation of generic convolutional and recurrent networks for sequence modeling’, arXiv:1803.01271 (2018) [Google Scholar]
- Keshmiri, R. & Shahri, A. M, ‘Intelligent abs fuzzy controller for diverse road surfaces’, World Academy of Science, Engineering and Technology 2(2), 62–67, (2007) [Google Scholar]
- Stallmann, M. J. & Els, P. S, ‘Parameterization and modelling of large off-road tyres for ride analyses: Part 2–parameterization and validation of tyre models’, Journal of Terramechanics 55, 85–94, (2014) [CrossRef] [Google Scholar]
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