Welcome!

At Control, Estimation, and Learning Laboratory (CELL), we design intelligent and adaptive controllers and estimators that thrive in unpredictable, real-world environments. We blend control theory, optimization, and machine learning to make robots, drones, and autonomous systems smarter, faster, and more robust.

What We Do

Control of complex cyber-physical systems

We develop adaptive and learning algorithms that keep systems stable and performing when everything else goes off track.

• From aircraft and multicopters to robots and autonomous vehicles.
• Real-time learning, sudden disturbance recovery, broad operational envelopes.

Data-driven estimation in dynamic systems

We craft computationally efficient, data-driven algorithms for state and parameter estimation in complex dynamic systems including atmospheric and flow models, enabling real-time prediction and control with improved accuracy and performance.

Why It Matters

Autonomous systems are everywhere. We push the frontier of intelligent systems by

• making systems that recover from the unexpected scenarios,
• reducing reliance on human intervention and human expertise,
• enabling smarter decision-making in high-stakes, real­-time scenarios, and
• bridging theory and application — hybridizing classical control design with cutting-edge learning for truly resilient performance.