Safety and Stability in Learning-Enabled Control
Robust-control and optimization tools for certifying neural control systems.
This research direction connects modern learning methods with formal control guarantees.
Focus areas:
- Stability analysis of neural-network-based controllers using quadratic constraints.
- Safety-aware imitation learning with formal stability/safety guarantees.
- Robust nonlinear control methods for systems with uncertainty and partial observability.
Representative outputs include publications in IEEE Transactions on Automatic Control, IEEE Control Systems Letters, and AAAI.