Project 1: 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.

Project 2: Connected and Automated Vehicles

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 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.

  • J. Lan, D. Zhao and D. Tian, Low Latency Predictive Cooperative Adaptive Cruise Control under Variable Road Geometry, IFAC-PapersOnLine, 53(2): 15116-15121, 2020.

  • J. Lan, D. Zhao and D. Tian, Robust Cooperative Adaptive Cruise Control of Vehicles on Banked and Curved Roads with Sensor Bias, American Control Conference, 2276-2281, 2020.

Project 3: Monitoring and Verification of Autonomous Systems

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

Selected references:

  • 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.