Our group develops optimization, control, and machine learning tools for large-scale distributed systems, such as power transmission networks, naval microgrids, and swarms of unmanned aerial vehicles.
Projects
- Scalable and Distributed Swarm Motion Planning via Integrated Optimization and Machine Learning, sponsored by NASA (Oct 2019 – Sept 2021),
- Reliable and Massively Scalable Grid Optimization via Low-complexity Convex Relaxation, sponsored by ARPA-E (Dec 2018 – June 2020),
- Massively Scalable Computational Methods for Power System Scheduling, sponsored by NSF (Dec 2018 – June 2020),
- Experimental and Hardware-in-the-loop Verification of Optimal and Reliable Control Methods for a Hybrid AC/DC Power Distribution Network, sponsored by ONR (July 2018 – Oct 2021),
- High-fidelity Optimization for Next-generation Shipboard Power Systems, sponsored by ONR (Mar 2018 – Feb 2021),