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Toward automated vehicle control beyond the stability limits via active drifting control

Professional drivers in drifting competitions demonstrate accurate control over a car's position and sideslip while operating in an open-loop unstable region of state space. Could similar approaches help autonomous cars contend with excursions past the stable handling limits, thereby improving overall safety outcomes?

Developing controllers for automated drifting could provide great insight into the general problem of fully utilizing the entire state space and ensuring that the widest possible range of maneuvers is available to an autonomous vehicle, should the need arise. When drifting, a standard assumption is removed - the orientation of the vehicle's velocity vector no longer follows that of the vehicle body. Although this at first seems daunting, the controller derivation instead reveals an opportunity: the rotation rate of the vehicle's velocity vector can now be used directly to track the path.

Then, by yawing the vehicle body faster or slower than its velocity vector, we can simultaneously control the sideslip of the vehicle.

The study will address the following objectives:

  1. Vehicle dynamic modelling and simulation with the tire force saturation
  2. Road-tire friction coefficient identification via vehicle state estimation
  3. Precise vehicle drifting control beyond the stability limits. Improve the vehicle path tracking performance under limited driving conditions


Mohanty, A., Zawislak, R., Bhamidipati, S., & Gao, G. (2021, September). Precise relative positioning for tandem drifting cars. In Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021) (pp. 113-124).

Zhao, T., Yurtsever, E., Chladny, R., & Rizzoni, G. (2021, September). Collision Avoidance with Transitional Drift Control. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 907-914). IEEE.

Subosits, J. K., & Gerdes, J. C. (2021). Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers. IEEE Transactions on Intelligent Vehicles, 6(3), 546-558.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Dong Zhang - Dr. Dong Zhang is a Lecturer in Automotive Design in the department of Mechanical and Aerospace Engineering. He leads the research on intelligent vehicle & transportation control systems, and founds the Brunel Racing Formula Student AI group.  He obtained his Ph.D. degree in vehicle dynamic control from University of Lincoln in October 2019. His PhD thesis was titled with ''A systematic approach to cooperative driving systems based on optimal control allocation''. After this, he worked as a research fellow in Centre for System Intelligence and Efficiency; and the Department of Mechanical and Aerospace Engineering, at Nanyang Technological University, Singapore from 2019 to 2020. During this period, his research was focusing to provide novel solutions to address the security challenges raised by intelligent and connected vehicles; and to develop a novel, integrated vehicle chassis control system for intelligent and automated electric vehicles (iAEVs). Before joining Brunel University London in 2021, he worked as a senior research project manager in scientific collaborative projects with car industry for developing the next-generation vehicle chassis control system. Dr. Zhang is welcoming and putting a special emphasis on the collaboration with car companies for developing human-centered automotive control systems and Advanced Driver Assistance Systems (ADAS).   Dr. Zhang's research interests strongly reside in the area of human-centered automotive control systems, intelligent vehicle/transportation control, game theory based driver-vehicle shared control systems, vehicle dynamic and safety control, adaptive vehicle motion control, and A.I based autonomous vehicle control. His research has resulted in more than thirty peer-reviewed papers in top journals and conferences, and about ten patents for invention.  Dr. Zhang has experience in co-supervising PhD students to completion. Please feel free to contact if you are seeking PhD opportunities in vehicle dynamic/safety control, intelligent vehicle /transportation control systems, A.I based autonomous vehicle control and driver-vehicle interaction.