Learning Theory for Estimation of Animal Motion Submanifolds. - Nathan Powell; Andrew J. Kurdila (CDC, 2020)

Koopman Methods for Estimation of Motion over Unknown, Regularly Embedded Submanifolds. - Nathan Powell; Bowei Liu; Andrew J. Kurdila (ACC, 2022)

Approximation of Koopman Operators: Irregular Domains and Positive Orbits. - Nathan Powell; Roger Liu; Jia Guo 004; Sai Tej Paruchuri; John A. Burns; Boone Estes; Andrew J. Kurdila (CDC, 2022)

Koopman Methods for Estimation of Animal Motions over Unknown, Regularly Embedded Submanifolds. - Nathan Powell; Bowei Liu; Andrew J. Kurdila (CoRR, 2022)

Convergence Rates for Approximations of Deterministic Koopman Operators via Inverse Problems. - Nathan Powell; Ali Bouland; John A. Burns; Andrew Kurdila (CDC, 2023)

The Power Function for Adaptive Control in Native Space Embedding. - Haoran Wang; Nathan Powell; Andrea L'Afflitto; Andrew J. Kurdila; John A. Burns (CDC, 2023)

MRAC With Adaptive Uncertainty Bounds via Operator-Valued Reproducing Kernels. - Haoran Wang; Brian Scurlock; Nathan Powell; Andrea L'Afflitto; Andrew J. Kurdila (IEEE Control. Syst. Lett., 2023)

Data-Driven Control Synthesis Using Koopman Operator: A Robust Approach. - Mert Eyüboglu; Nathan Powell; Alireza Karimi (ACC, 2024)

Newton Bases and Event-Triggered Adaptive Control in Native Spaces. - Nathan Powell; Andrew J. Kurdila; Andrea L'Afflitto; Haoran Wang; Jia Guo 004 (ACC, 2024)

Invariance and Approximation of Koopman Operators in Native Spaces. - Nathan Powell; Sai Tej Paruchuri; Ali Bouland; Shengyuan Niu; Andrew Kurdila (ACC, 2024)