Biography
Kanghui He is currently a Postdoctoral Researcher in the Department of Engineering Science at the University of Oxford, supported by an independent Rubicon Postdoctoral Fellowship funded by the Netherlands Organisation for Scientific Research (NWO). He received his PhD from the Delft Center for Systems and Control at Delft University of Technology, the Netherlands. He received the M.Sc. and B.Sc. degrees from Beihang University, China. He was a research assistant in the Department of Automation, Tsinghua University. At the University of Oxford, his research focuses on developing artificial intelligence-driven decision-making algorithms that enable autonomous systems to operate more safely, reliably, and efficiently in uncertain environments, under the supervision of Prof. Antonis Papachristodoulou.
Current Research Projects
Integrated Learning and Optimisation for Safety-Critical Control
A major challenge in the control of large-scale safety-critical systems under uncertainty, such as autonomous vehicles, is balancing computational efficiency and safety performance (satisfaction of given safety constraints). To address this challenge, the project will develop a new, efficient, and practically reliable control framework that combines optimisation-based and learning-based control.
Research Interests
Control theory
Machine learning
Autonomous systems
Research Group
Related Academic
K. He, S. Shi, T. van den Boom, and B. De Schutter, “State-action control barrier functions: Imposing safety on learning-based control with low online computational costs”, IEEE Transactions on Automatic Control, vol. 71, no. 5, p. 3365–3371, 2025.
K. He, S. Shi, T. van den Boom, and B. De Schutter, “Approximate dynamic programming for constrained linear systems: A piecewise quadratic approximation approach”, Automatica, vol. 160, p. 111 456, 2024
K. He, S. Shi, T. van den Boom, and B. De Schutter, “Approximate dynamic programming for constrained piecewise affine systems with stability and safety guarantees”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 3, pp. 1722–1734, 2024.
K. He, Y. Deng, G. Wang, X. Sun, Y. Sun, and Z. Chen, “Learning-based trajectory tracking and balance control for bicycle robots with a pendulum: A Gaussian process approach,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 634–644, 2022.
K. He, C. Dong, and Q. Wang, “Active disturbance rejection control for uncertain nonlinear systems with sporadic measurements,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 5, pp. 893–906, 2022.
K. He, C. Dong, and Q. Wang, “Active disturbance rejection adaptive control for uncertain nonlinear systems with unknown time-varying dead-zone input,” Asian Journal of Control, vol. 24, no. 3, pp. 1209–1222, 2022.
K. He, C. Dong, and Q. Wang, “Anti-disturbance dynamic inversion backstepping control for uncertain pure-feedback systems via multiple extended state observers,” Journal of the Franklin Institute, vol. 358, no. 13, pp. 6385–6407, 2021.
K. He, C. Dong, and Q. Wang, “Cascade integral predictors and feedback control for nonlinear systems with unknown time-varying input-delays,” International Journal of Control, Automation and Systems, vol. 18, no. 5, pp. 1128–1138, 2020.
Y. Li, K. He, M. Ma, X. Qi, Y. Bai, S. Liu, Y. Gao, F. Lyu, C. Jia, B. Zhao, H. Guo, and J. Wang, “Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy,” Medical Physics, vol. 47, no. 12, pp. 6540–6550, 2020.
