The Energy, Power, Control, andNetworks (EPCN) Program supports innovative research in modeling, optimization, learning, adaptation, and control of networked multi-agent systems, higher-level decision making, and dynamic resource allocation, as well as risk management in the presence of uncertainty, sub-system failures, and stochastic disturbances. EPCN also invests in novel machine learning algorithms and analysis, adaptive dynamic programming, brain-like networked architectures performing real-time learning, and neuromorphic engineering. EPCN’s goal is to encourage research on emerging technologies and applications including energy, transportation, robotics, and biomedical devices & systems. EPCN also emphasizes electric power systems, including generation, transmission, storage, and integration of renewable energy sources into the grid; power electronics and drives; battery management systems; hybrid and electric vehicles; and understanding of the interplay of power systems with associated regulatory & economic structures and with consumer behavior.
Areas managed by Program Directors (please contact Program Directors listed in the
EPCN staff directory for areas of interest):
Control Systems
- Distributed Control and Optimization
- Networked Multi-Agent Systems
- Stochastic, Hybrid, Nonlinear Systems
- Dynamic Data-Enabled Learning, Decision and Control
- Cyber-Physical Control Systems
- Applications (Biomedical, Transportation, Robotics)
Energy and Power Systems
- Solar, Wind, and Storage Devices Integration with the Grid
- Monitoring, Protection and Resilient Operation of Grid
- Power Grid Cybersecurity
- Market design, Consumer Behavior, Regulatory Policy
- Microgrids
- Energy Efficient Buildings and Communities
Power Electronics Systems
- Advanced Power Electronics and Electric Machines
- Electric and Hybrid Electric Vehicles
- Energy Harvesting, Storage Devices and Systems
- Innovative Grid-tied Power Electronic Converters
Learning and Adaptive Systems