My research interests mainly focus on the theory and algorithms for estimation, learning, and control of nonlinear dynamical systems, with a strong emphasis on robotics and autonomous systems. I aim to develop mathematically rigorous methods with provable guarantees, while ensuring their applicability to real-world robotic platforms.
This line of research studies real-time state estimation algorithms for nonlinear control systems, with particular interest in convergence guarantees and robustness. I am interested in various topics in this field, including
This theme explores data-driven modeling and learning techniques for dynamical systems, bridging system identification and modern learning theory.
I investigate nonlinear control design methods from the following perspectives.
My research applies estimation, learning, and control theories to complex robotic systems operating in uncertain environments.