Loughborough University | United Kingdom
Dr Jingjing Jiang is a distinguished researcher in intelligent mobility and autonomous systems, currently a Senior Lecturer in Intelligent Mobility and Autonomous Vehicles at the Department of Aeronautical and Automotive Engineering, Loughborough University. She holds a BE in Electronic and Electrical Engineering an MSc in Control Engineering , and a PhD in Control Engineering (Imperial College London, thesis: Shared Control for Systems with Constraints). After her doctoral work she served as a Research Associate in the Department of Electrical and Electronic Engineering at Imperial College London before joining Loughborough University as Lecturer in 2018 and being promoted to Senior Lecturer. Her research interest spans trustworthy control design and rigorous closed-loop performance analysis for intelligent systems and autonomous vehicles, combining classical control theory with modern data-driven models and algorithms, and emphasising both trial-based validation and top-down safety and reliability guarantees. Her work has been recognised for bridging fundamental research and real-world application in mobility systems. She continues to drive innovation in autonomous mobility and system safety, contributing to the future of reliable intelligent transport.
Featured Publications
Cao, S., Sun, L., Jiang, J., & Zuo, Z. (2021). Reinforcement learning-based fixed-time trajectory tracking control for uncertain robotic manipulators with input saturation. IEEE Transactions on Neural Networks and Learning Systems, 34(8), 4584–4595.
Jiang, J., & Astolfi, A. (2018). Lateral control of an autonomous vehicle. IEEE Transactions on Intelligent Vehicles, 3(2), 228–237.
Fu, H., Jiang, J., Hu, S., Rao, J., & Theodossiades, S. (2023). A multi-stable ultra-low frequency energy harvester using a nonlinear pendulum and piezoelectric transduction for self-powered sensing. Mechanical Systems and Signal Processing, 189, 110034.
Jiang, J., & Astolfi, A. (2020). Stabilization of a class of underactuated nonlinear systems via underactuated back-stepping. IEEE Transactions on Automatic Control, 66(11), 5429–5435.
Hu, J., Lin, Y., Li, J., Hou, Z., Chu, L., Zhao, D., Zhou, Q., Jiang, J., & Zhang, Y. (2024). Performance analysis of AI-based energy management in electric vehicles: A case study on classic reinforcement learning. Energy Conversion and Management, 300, 117964.