Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling Using Torque Vectoring (2405.10847v1)

Published 17 May 2024 in cs.RO, cs.SY, and eess.SY

Abstract: This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit of handling. The proposed algorithm combines motion planning, path tracking and vehicle stability objectives, prioritising collision avoidance in emergencies. The controller's prediction model is a nonlinear double-track vehicle model based on an extended Fiala tyre to capture the nonlinear coupled longitudinal and lateral dynamics. The controller computes the optimal steering angle and the longitudinal forces per each of the four wheels to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Thanks to the optimisation of the longitudinal tyre forces, the proposed controller can produce an extra yaw moment, increasing the vehicle's lateral agility to avoid obstacles while keeping the vehicle stable. The optimal forces are constrained in the tyre friction circle not to exceed the tyres and vehicle capabilities. In a high-fidelity simulation environment, we demonstrate the benefits of torque vectoring, showing that our proposed approach is capable of successfully avoiding obstacles and keeping the vehicle stable while driving a double-lane change manoeuvre, in comparison to baselines lacking torque vectoring or collision avoidance prioritisation.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (25)
  1. N. Chowdhri, L. Ferranti, F. Iribarren, and B. Shyrokau, “Integrated nonlinear model predictive control for automated driving,” Control Engineering Practice, vol. 106, 2021.
  2. J. K. Subosits and J. C. Gerdes, “Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers,” IEEE Transactions on Intelligent Vehicles, vol. 6, no. 3, pp. 546–558, 2021.
  3. R. Hajiloo, M. Abroshan, A. Khajepour, A. Kasaiezadeh, and S.-K. Chen, “Integrated steering and differential braking for emergency collision avoidance in autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 3167–3178, 2021.
  4. A. Bertipaglia, M. Alirezaei, R. Happee, and B. Shyrokau, “Model predictive contouring control for vehicle obstacle avoidance at the limit of handling,” arXiv preprint arXiv:2308.06742, 2023.
  5. A. Liniger, A. Domahidi, and M. Morari, “Optimization-based autonomous racing of 1: 43 scale rc cars,” Optimal Control Applications and Methods, vol. 36, no. 5, pp. 628–647, 2015.
  6. P. Falcone, F. Borrelli, H. E. Tseng, J. Asgari, and D. Hrovat, “A hierarchical model predictive control framework for autonomous ground vehicles,” American Control Conference, 2008.
  7. D. Lenssen, A. Bertipaglia, F. Santafe, and B. Shyrokau, “Combined path following and vehicle stability control using model predictive control,” SAE Technical Paper 2023-01-0645, Tech. Rep., 2023.
  8. Y. Gao, A. Gray, A. Carvalho, E. Tseng, and F. Borrelli, “Robust nonlinear predictive control for semiautonomous ground vehicles,” American Control Conference, 2014.
  9. M. Brown and J. C. Gerdes, “Coordinating tire forces to avoid obstacles using nonlinear model predictive control,” IEEE Transactions on Intelligent Vehicles, vol. 5, no. 1, pp. 21–31, 2019.
  10. W. Degel, S. Lupberger, D. Odenthal, and N. Bajcinca, “Scalable slip control with torque vectoring including input-to-state stability analysis,” IEEE Transactions on Control Systems Technology, vol. 31, no. 3, pp. 1250–1265, 2023.
  11. L. De Novellis, A. Sorniotti, P. Gruber, and A. Pennycott, “Comparison of feedback control techniques for torque-vectoring control of fully electric vehicles,” IEEE Transactions on Vehicular Technology, vol. 63, no. 8, pp. 3612–3623, 2014.
  12. Y. Ren, L. Zheng, and A. Khajepour, “Integrated model predictive and torque vectoring control for path tracking of 4-wheel-driven autonomous vehicles,” IET Intelligent Transport Systems, vol. 13, no. 1, pp. 98–107, 2019.
  13. C. Chatzikomis, A. Sorniotti, P. Gruber, M. Zanchetta, D. Willans, and B. Balcombe, “Comparison of path tracking and torque-vectoring controllers for autonomous electric vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 3, no. 4, pp. 559–570, 2018.
  14. P. Stano, D. Tavernini, U. Montanaro, M. Tufo, G. Fiengo, L. Novella, and A. Sorniotti, “Enhanced active safety through integrated autonomous drifting and direct yaw moment control via nonlinear model predictive control,” IEEE Transactions on Intelligent Vehicles, pp. 1–17, 2023.
  15. D. Kasinathan, A. Kasaiezadeh, A. Wong, A. Khajepour, S.-K. Chen, and B. Litkouhi, “An optimal torque vectoring control for vehicle applications via real-time constraints,” IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 4368–4378, 2015.
  16. T. P. Weber and J. C. Gerdes, “Modeling and control for dynamic drifting trajectories,” IEEE Transactions on Intelligent Vehicles, pp. 1–11, 2023.
  17. V. A. Laurense and J. C. Gerdes, “Long-horizon vehicle motion planning and control through serially cascaded model complexity,” IEEE Transactions on Control Systems Technology, vol. 30, no. 1, pp. 166–179, 2022.
  18. A. Bertipaglia, B. Shyrokau, M. Alirezaei, and R. Happee, “A two-stage bayesian optimisation for automatic tuning of an unscented kalman filter for vehicle sideslip angle estimation,” in IEEE Intelligent Vehicles Symposium, 2022.
  19. A. Bertipaglia, D. de Mol, M. Alirezaei, R. Happee, and B. Shyrokau, “Model-based vs data-driven estimation of vehicle sideslip angle and benefits of tyre force measurements,” Preprint arXiv:2206.15119, 2022.
  20. A. Bertipaglia, M. Alirezaei, R. Happee, and B. Shyrokau, “An unscented kalman filter-informed neural network for vehicle sideslip angle estimation,” IEEE Transactions on Vehicular Technology, pp. 1–15, 2024.
  21. A. Zanelli, A. Domahidi, J. Jerez, and M. Morari, “Forces nlp: an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs,” International Journal of Control, pp. 1–17, 2017.
  22. C. Yu, Y. Zheng, B. Shyrokau, and V. Ivanov, “Mpc-based path following design for automated vehicles with rear wheel steering,” in IEEE International Conference on Mechatronics, 2021, pp. 1–6.
  23. A. Parra, D. Tavernini, P. Gruber, A. Sorniotti, A. Zubizarreta, and J. Pérez, “On nonlinear model predictive control for energy-efficient torque-vectoring,” IEEE Transactions on Vehicular Technology, vol. 70, no. 1, pp. 173–188, 2021.
  24. V. Vidal, P. Stano, G. Tavolo, M. Dhaens, D. Tavernini, P. Gruber, and A. Sorniotti, “On pre-emptive in-wheel motor control for reducing the longitudinal acceleration oscillations caused by road irregularities,” IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9322–9337, 2022.
  25. L. Shao, D. Tavernini, A. E. Hartavi Karci, and A. Sorniotti, “Design and optimisation of energy-efficient pm-assisted synchronous reluctance machines for electric vehicles,” IET Electric Power Applications, vol. 17, no. 6, pp. 788–801, 2023.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Alberto Bertipaglia (6 papers)
  2. Davide Tavernini (6 papers)
  3. Umberto Montanaro (6 papers)
  4. Mohsen Alirezaei (8 papers)
  5. Riender Happee (17 papers)
  6. Aldo Sorniotti (5 papers)
  7. Barys Shyrokau (16 papers)
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets