AI-powered Complex System Optimal Control and State Estimation

This workshop focuses on cutting-edge advancements in applying artificial intelligence to the optimal control and predictive analysis of complex systems. As modern infrastructures and networks grow increasingly dynamic and interconnected, traditional control methods often fall short. Leveraging AI techniques, such as deep learning, reinforcement learning, and hybrid models, this workshop explores intelligent strategies for real-time decision-making, stability enhancement, and performance index forecasting in multi-dimensional, nonlinear systems. It brings together researchers and practitioners to discuss novel methodologies, benchmark studies, and real-world applications in energy systems, robotics, transportation, and beyond. Topics of interest include, but are not limited to:

1. AI-augmented optimal control for nonlinear, and constrained systems

2. State estimation and filtering with deep learning

3. Reinforcement learning for control with constraints, safety, and stability

4. Uncertainty quantification for control and estimation

5. Multi-agent and networked systems

6. Real-time decision-making and scalable optimization

Hybrid/physics-informed models: neural ODE/PDE, operator learning

Chair:  

Kai Gong, Zhejiang University, China

Dr. Kai Gong is a postdoctoral researcher at the College of Control Science and Engineering, Zhejiang University, co-head of the Advanced AI and Optimization Control Laboratory (AAICO Lab), and a member of the State Key Laboratory of Industrial Control Technology and the National Engineering Research Center of Industrial Automation. He has published over 15 papers in journals such as IEEE Transactions on Sustainable Energy and held over 30 invention patents, with applications spanning power grids, nuclear energy, and smart manufacturing. His current and future research interests extend to the interdisciplinary application of convex optimization, artificial intelligence, and reinforcement learning in power systems, cancer chemotherapy, drug discovery, and chemical characterization.

Co-chair：

Xu Wang, Shanghai Jiao Tong University, China

Dr. Xu Wang received the B.S. degree in electrical engineering from Southeast University, China, in 2010 and the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, China, in 2016. He was a Postdoctoral Associate at in the Robert W. Galvin Center for Electricity Innovation at Illinois Institute of Technology (IIT), Chicago, USA, from 2016-2018. Currently, he is an Associate Professor with the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China. His research interests include resilient distribution system, power system economics and optimization.