Academic Staff

Research interests

Unmanned aerial vehicles, autonomous vehicles,  sensor fusion, navigation, computer vision, dynamics, advanced control, autopilot, situational awareness, decision making, path planning, mission planning, fault detection and condition monitoring, safety, Bayesian inference, dual control, active learning, collision avoidance, system integration, verification and validation, uncertainty, autonomous systems, safety, flight tests, agriculture, precision farming, environment monitoring, remote sensing, intelligent manufacturing, wave energy.  

Professor Chen currently holds Chair Professor of Robotics and Autonomous Systems and the Director of Research Centre for Low Altitude Economy (RCLAE). He is an international renowned scholar at the forefront of control and autonomous vehicles.  He made pioneering and long-lasting contributions in nonlinear disturbance observer-based control, model predictive control and unmanned aerial vehicles. Ranging from large scale fixed-wing cargos to helicopters and small remote sensing quadrotors, he has been working in unmanned aerial vehicles for more than 20 years.   With the expertise in the development and application of control and autonomous system technologies in a wide range of areas from aerospace, automotive to agriculture, environment monitoring and disaster managements, he published more than 350 papers in international journals and conferences with over 24,000 citation and h-index of 63 (Google Scholars).  Moreover, he attracted wide support from the U.K. government and industry with a grant portfolio of over £12 million which showed his strong impact in academic research and industry transfer. In the “World’s Top 2% Scientists” by Stanford University, he was ranked 30th globally and 2nd in the UK for being the most influential researcher in the field of Industrial Engineering & Automation in 2024.

Professor Chen is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Institution of Mechanical Engineers (IMechE), The Institution of Engineering and Technology (IET) and the Advance HE (formerly the Higher Education Academy).  He was also awarded the Established Career Fellowship from the Engineering and Physical Sciences Research Council (EPSRC) from 2020 to 2025, and the only awardee in the subject of Control Engineering so far.  Besides, he was elected as Changjiang Chair Professor by the Ministry of Education of the People’s Republic of China in 2013.

Professor Chen obtained his Bachelor of Engineering degree in Industrial Automation from Jiangsu University, China in 1986.  He further received his Master of Science degree and Doctor of Philosophy degree, both in Control Engineering from Northeastern University, China in 1989 and 1991 respectively.  Professor Chen started his academic career in Nanjing University of Aeronautics and Astronautics, China as a Lecturer in the Department of Automatic Control in 1991 and was then promoted to Associate Professor in the same department in 1993.  In 1997, he moved to the University of Glasgow, Scotland to work as a Research Associate and was subsequently converted to academic track as a Lecturer in 2000.  Since late 2000, he joined the Department of Aeronautical and Automotive Engineering at Loughborough University, U.K. as a Lecturer and was then promoted to Senior Lecturer in 2006. Since 2012, he has been appointed Chair Professor in Autonomous Vehicles.  He set up the Loughborough Centre for Autonomous Systems and Control in 2012 and developed it into a centre of a significant international profile.  

After joining PolyU, he is leading the university wide effort to develop the Research Centre for Low Altitude Economy (RCLAE) to be a world leader in research and development, education and training, and knowledge transfer.  

Figure 1 Personal View of Low-Altitude Economy (LAE) Eco-Systems

Representative publications in last 5 years

Philosophical vision of future autonomous control systems

1. W-H Chen (2024). Goal-Oriented Control Systems (GOCS): from HOW to WHAT. IEEE/CAA Journal of Automatica Sinica. Vol.11, N0.4, pp.816-819. 10.1109/JAS.2024.124323.

This is the first paper that describes a conceptual framework for design and analysis of robotics and autonomous systems. It summarises my vison of new fundamental design principle and underpinning control theories for future high levels of automation. This work was supported by the Established Career Fellowship award of UK Engineering and Physical Sciences Research Council (2020-2026).

Active learning and dual control

2. W-H Chen (2022). Perspective View of Autonomous Control in Unknown Environment: Dual Control for Exploitation and Exploration vs Reinforcement Learning. Neurocomputing. Vol.497, pp.50-63. https://doi.org/10.1016/j.neucom.2022.04.131

Dual Control for Exploration and Exploitation (DCEE) provides an optimal trade-off between making use of the existing information and exploring unknowns to get more information for long term benefits. At a high level, this paper compares this new active learning method with widely unknown Reinforcement Learning and demonstrating its benefit in developing autonomous systems under uncertain operational environments. It also establishes its link with natural intelligence in neuroscience, more specifically active inference.    

3. W-H Chen, C Rhode and C Liu (2021). Dual control for exploration and exploitation in autonomous search. Automatica. Vol.133, No.11, 109851. doi.org/10.1016/j.automatica.2021.109851.

Motivated by autonomous search of hazardous resources, this is the first paper where the Dual Control for Exploration and Exploitation concept was proposed. Experiment tests demonstrates that it significantly outperforms both control theoretic and  information theoretic approaches in terms of the success rate and search time.

New framework of autonomous decision-making design for unmanned vehicles(aerial, ground, etc)

4. X-F Wang, J Jiang, and W-H Chen (2025). A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPC. IEEE Transactions on Cybernetics, DOI:10.1109/TCYB.2025.3535159.

A complicated autonomous system usually has a hierarchical architecture consisting of multilayers with more physics related function at lower levels and cognition related functions at upper levels.   This paper for the first time proposes a complete framework in developing autonomous decision making at the high-levels while taking into account low-level continuous dynamics. Motivated by and demonstrated in autonomous driving, this comprehensive framework consists of new mathematic modelling based on Hybrid Markov Decision Process, design of the decision maker using the model predictive control concept, and rigorous analysis of its stability and safety.

Latest progress in disturbance observer and motion control

5. S Li, J Yang, M Iwasaki and W-H Chen (2025). Hierarchical Disturbance/Uncertainty Estimation and Attenuation for Integrated Modelling and Motion Control: Overview and Perspectives. IEEE Transactions on Mechatronics. DOI:10.1109/TMECH.2024.3515084.

Since pioneering the most widely used nonlinear disturbance observer design technique and the nonlinear disturbance observer-based control (DOBC) framework, we have been leading the effort of the international community in developing fundamental theories and practical application of disturbance/uncertainty estimation and attenuation more than 20 years. This latest overview paper summarises recent progress and our perspective of this growing area.