My research bridges rigorous control theory and experimental robotics. Theoretical contributions are motivated by limitations that arise in complex autonomous systems, and applications serve as a proving ground for new methods in adaptive, robust, optimal, and data-driven control.
This line of work develops adaptive control methods that learn uncertainty models from data, including reproducing-kernel Hilbert space methods, native-space embeddings, Koopman-operator tools, and data-informed basis selection.
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This research addresses systems whose dynamics switch across modes or include impacts, constraints, and other hybrid phenomena. The goal is to achieve reliable adaptive performance despite time-varying uncertainties and nonsmooth behavior.
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This topic focuses on adaptive control systems that enforce state, input, tracking-error, transient-performance, and user-defined safety constraints while retaining robustness to uncertainty.
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This research develops optimal and game-theoretic control methods for nonlinear systems, including finite-time stabilization, semistabilization, singular control, policy iteration, and critic/value-function approximation.
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Aerial robotics applications include autopilot design, guidance and navigation, tactical mapping, tailsitter and tilt-rotor control, payload transport, shipboard landing, and open-source flight-stack development.
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Ground robotics applications focus on terrain-aware autonomy, perception, terramechanics, off-road mobility, and robust control for autonomous ground vehicles operating in uncertain environments.
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