Carl Folkestad
Papers
4
Total Citations
74
H-Index
3
About
Carl Folkestad is a leading researcher at the intersection of data-driven control, nonlinear dynamics, and safety-critical autonomy. His work centers on leveraging Koopman operator theory to transform complex, nonlinear robotic systems into tractable, high-dimensional linear or bilinear models—enabling real-time optimal and safe control. Folkestad’s major contributions include pioneering the synthesis of Control Barrier Functions (CBFs) with Koopman operators for safety-critical control, a breakthrough that guarantees system safety without the computational burden of traditional invariant set computation. His highly cited 2020 paper on this topic (36 citations) has become a foundational reference in the field. He further advanced the state of the art with KoopNet (2022, 32 citations), which jointly learns Koopman bilinear models and function dictionaries, demonstrating exceptional performance in quadrotor trajectory tracking. Folkestad’s work on Koopman-based Nonlinear Model Predictive Control (NMPC) and episodic Koopman learning for fast multirotor landing showcases his ability to bridge theory and practice, enabling agile, safe, and efficient autonomous systems. His research is widely recognized for making Koopman methods practical for real-world robotic applications.
Research Focus
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Top Papers
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