Fault-Tolerant Autonomous Systems

This research aims to developing efficient model-based/learning-based techniques for the diagnosis, compensation, and mitigation of faults, cyber-attacks, or other abnormal behaviours of autonomous systems in real-world environments.

Selected references:

J. Lan and R. J. Patton, Robust Integration of Model-based Fault Estimation and Fault-Tolerant Control, Springer, 2020.
J. Lan and R. J. Patton, An iterative strategy for robust integration of fault estimation and fault-tolerant control, Automatica, 145, 110556, 2022.
J. Lan, Asymptotic Estimation of State and Faults for Linear Systems with Unknown Perturbations, Automatica. 108955, 2020.
J. Lan and R. J. Patton, A Decoupling Approach to Integrated Fault-Tolerant Control for Linear Systems with Non-Differentiable Faults, Automatica, 89, 290–299, 2018.
J. Lan, R. J. Patton and X. Zhu, Fault-Tolerant Wind Turbine Pitch Control using Adaptive Sliding Mode Estimation, Renewable Energy, 116 (Part B), 219-231, 2018.
J. Lan and R. J. Patton, A New Strategy for Integration of Fault Estimation within Fault-Tolerant Control, Automatica, 69, 48–59, 2016.

Intelligent Transportation Systems

This research aims to developing model-based/learning-based techniques to ensure safety, robustness, and functionality of connected and automated vehicles. The ultimate goal is to promise the establishment of efficient and safe future traffic systems.

Selected references:

J. Lan. Data-driven cooperative adaptive cruise control for unknown nonlinear vehicle platoons. IET Intelligent Transport Systems, https://doi.org/10.1049/itr2.12556, 2024.
J. Lan and D. Zhao, Safe and robust data‐driven cooperative control policy for mixed vehicle platoons , International Journal of Robust and Nonlinear Control, https://doi.org/10.1002/rnc.6412, 2022.
J. Lan, D. Zhao, and D. Tian, Data-Driven Robust Predictive Control for Mixed Vehicle Platoons using Noisy Measurement, IEEE Transactions on Intelligent Transportation System. DOI:, 10.1109/TITS.2021.3128406, 2021.
J. Lan and D. Zhao, Min-Max Model Predictive Vehicle Platooning with Communication Delay, IEEE Transactions on Vehicular Technology, 69(11): 12570 - 12584, 2020.
J. Lan and D. Zhao, Robust Model Predictive Control for Nonlinear Parameter Varying Systems without Computational Delay, International Journal of Robust and Nonlinear Control, 31(17), 8273-8294, 2021.

Safe AI and Autonomous Systems

This research aims to developing efficient techniques for assuring the safety, trustworthy, robustness, and functionality of learning-enabled autonomous systems. The research investigates the aspects including runtime monitoring, formal verification, safe reinforcement learning.

Selected references:

J. Jiang, J. Lan, F. Leofante, A. Rago, and F. Toni. Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation, in Proceedings of the 15th Asian Conference on Machine Learning, PMLR 222:582-597, 2024.
J. Lan, S. Zhan, R. Patton, and X. Zhao. Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems, in IFAC-PapersOnLine, 58(4), 258 – 263, 2024.
H. Hu, J. Lan, and C. Liu. Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation, in Proc. 6th Annual Learning for Dynamics & Control Conference, PMLR 242: 92 – 103, 2024.
J. Lan, Y. Zheng, and A. Lomuscio. Iteratively Enhanced Semidefinite Relaxations for Efficient Neural Network Verification, in Proc. AAAI, 37(12): 14937-14945, 2023.
J. Lan, B. Bruckner, and A. Lomuscio. A Semidefinite Relaxation based Branch-and-Bound Method for Tight Neural Network Verification, in Proc. AAAI, 37(12): 14946-14954, 2023.
J. Lan, Y. Zheng, and A. Lomuscio. "Tight neural network verification via semidefinite relaxations and linear reformulations." In Proceedings of the AAAI Conference on Artificial Intelligence, 36(7): 7272-7280. 2022.

Vision-based autonomous robots

This research aims to integrate perception and control for truly satisfactory autonomous robots (e.g. drones, mobile robots, ground vehicles). The key technical developments focus on computer vision, decision making, path planning, and control algorithms using model- or learning-based frameworks.

Selected references:

Z. Lin, Q. Zhang, Z. Tian, P. Yu, Z. Ye, H. Zhuang, J. Lan. SLAM2: Simultaneous Localization and Multimode Mapping for Indoor Dynamic Environments. Pattern Recognition, 158: 111054, 2025.
Z. Lin, Z. Tian, Q. Zhang, H. Zhuang, J. Lan. Enhanced Visual SLAM for Collision-Free Driving with Lightweight Autonomous Cars. Sensors, 24(19), 6258, 2024
Z. Lin, Q. Zhang, Zhen Tian, P. Yu, and J. Lan. DPL-SLAM: Enhancing Dynamic Point-Line SLAM through Dense Semantic Methods. IEEE Sensor Journal, 24(9): 14596 – 14607, 2024.