Vision-based control of micro-air-vehicles: progress and problems in estimation. - Andrew Kurdila; M. Nechyba; Richard Prazenica; Wolfgang Dahmen; Peter Binev; Ronald A. DeVore; Robert C. Sharpley (CDC, 2004)

Vision-based geometry estimation and receding horizon path planning for UAVs operating in urban environments. - Richard Prazenica; Andrew Kurdila; Robert Caldwell Sharpley; J. Evers (ACC, 2006)

Adaptive control of non-affine uncertain systems. - Vahram Stepanyan; Andrew Kurdila (CDC, 2007)

Analysis of local observability for feature localization in a maritime environment using an omnidirectional camera. - Bin Xu 007; Daniel J. Stilwell; Aditya S. Gadre; Andrew Kurdila (IROS, 2007)

Nonlinear flight control in the presence of structural changes and external disturbances. - Vahram Stepanyan; Andrew Kurdila (ACC, 2008)

On the averaging method for affine in control systems. - Vahram Stepanyan; Andrew Kurdila (CDC, 2008)

L1 adaptive controller for air-breathing hypersonic vehicle with flexible body dynamics. - Yu Lei; Chengyu Cao; Eugene M. Cliff; Naira Hovakimyan; Andrew Kurdila; Kevin A. Wise (ACC, 2009)

A hybrid receding horizon control method for path planning in uncertain environments. - Bin Xu 007; Andrew Kurdila; Daniel J. Stilwell (IROS, 2009)

Efficient computation of level sets for path planning. - Bin Xu 007; Daniel J. Stilwell; Andrew Kurdila (IROS, 2009)

Asymptotic Tracking of Uncertain Systems With Continuous Control Using Adaptive Bounding. - Vahram Stepanyan; Andrew Kurdila (IEEE Trans. Neural Networks, 2009)

Pursuit-evasion with acoustic sensing using one step nash equilibria. - Brian J. Goode; Andrew Kurdila; Mike Roan (ACC, 2010)

Near-Optimal Approximation Rates for Distribution Free Learning with Exponentially, Mixing Observations. - Andrew J. Kurdila; Bin Xu 007 (ACC, 2010)

Adaptive fuzzy control of switched objective functions in pursuit-evasion scenarios. - Brian J. Goode; Andrew Kurdila; Mike Roan (CDC, 2010)

Geometric ergodicity of the distributional consensus problem in vehicle network control. - Bin Xu 007; Andrew Kurdila; Daniel J. Stilwell (CDC, 2010)

A real-time grid map generation and object classification for ground-based 3D LIDAR data using image analysis techniques. - Sang-Mook Lee; Jeong Joon Im; Bo-Hee Lee; Alexander Leonessa; Andrew Kurdila (ICIP, 2010)

A receding horizon controller for motion planning in the presence of moving obstacles. - Bin Xu 007; Daniel J. Stilwell; Andrew Kurdila (ICRA, 2010)

A graph theoretical approach toward a switched feedback controller for pursuit-evasion scenarios. - Brian J. Goode; Andrew Kurdila; Mike Roan (ACC, 2011)

A receding horizon approach to generating dynamically feasible plans for vehicles that operate over large areas. - Daniel J. Stilwell; Aditya S. Gadre; Andrew Kurdila (IROS, 2011)

Learning theory with consensus in reproducing kernel Hilbert spaces. - Zhaoda Deng; Jessica Gregory; Andrew Kurdila (ACC, 2012)

A novel functional regression based estimation and control algorithm. - Yu Lei; Andrew Kurdila (ACC, 2012)

Adaptive control for bioinspired flapping wing robots. - J. Bayandor; G. Bledt; Shirin Dadashi; Andrew Kurdila; I. Murphy; Yu Lei (ACC, 2013)

Adaptive control via embedding in reproducing kernel Hilbert spaces. - Andrew Kurdila; Yu Lei (ACC, 2013)

Fast Path Re-planning Based on Fast Marching and Level Sets. - Bin Xu 007; Daniel J. Stilwell; Andrew Kurdila (J. Intell. Robotic Syst., 2013)

Adaptive control for flapping wing robots with history dependent, unsteady aerodynamics. - Shirin Dadashi; J. Feaster; G. Bledt; J. Bayandor; Francine Battaglia; Andrew Kurdila (ACC, 2015)

Error estimates for multiwavelet approximations of a class of history dependent operators. - Shirin Dadashi; Parag Bobade; Andrew J. Kurdila (CDC, 2016)

Adaptive estimation in reproducing kernel Hilbert spaces. - Parag Bobade; Suprotim Majumdar; Savio Pereira; Andrew J. Kurdila; John B. Ferris (ACC, 2017)

Learning theory and empirical potentials for modeling discrete mechanics. - Shirin Dadashi; Hunter G. McClelland; Andrew Kurdila (ACC, 2017)

Piezoelectric composite subordinate oscillator arrays and frequency response shaping for passive vibration attenuation. - Sai Tej Paruchuri; John Sterling; Andrew Kurdila; Joseph Vignola (CCTA, 2017)

Gaussian process dynamic modeling of bat flapping flight. - Matthew J. Bender; Xu Yang; Hui Chen; Andrew Kurdila; Rolf Müller 001 (ICIP, 2017)

Multi-agent adaptive estimation with consensus in reproducing kernel Hilbert spaces. - Parag Bobade; Dimitra Panagou; Andrew J. Kurdila (ECC, 2019)

Adaptive estimation for nonlinear systems using reproducing kernel Hilbert spaces. - Parag Bobade; Suprotim Majumdar; Savio Pereira; Andrew J. Kurdila; John B. Ferris (Adv. Comput. Math., 2019)

Persistence of Excitation in Reproducing Kernel Hilbert Spaces, Positive Limit Sets, and Smooth Manifolds. - Andrew J. Kurdila; Jia Guo 004; Sai Tej Paruchuri; Parag Bobade (CoRR, 2019)

Persistence of Excitation in Uniformly Embedded Reproducing Kernel Hilbert (RKH) Spaces. - Jia Guo 004; Sai Tej Paruchuri; Andrew J. Kurdila (ACC, 2020)

Approximations of the Reproducing Kernel Hilbert Space (RKHS) Embedding Method over Manifolds. - Jia Guo 004; Sai Tej Paruchuri; Andrew J. Kurdila (CDC, 2020)

Intrinsic and Extrinsic Approximation of Koopman Operators over Manifolds. - Sai Tej Paruchuri; Jia Guo 004; Michael E. Kepler; Tim Ryan; Haoran Wang; Andrew J. Kurdila; Daniel J. Stilwell (CDC, 2020)

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

RKHS Embedding for Estimating Nonlinear Piezoelectric Systems. - Sai Tej Paruchuri; Jia Guo 004; Andrew J. Kurdila (CoRR, 2020)

Kernel Center Adaptation in the Reproducing Kernel Hilbert Space Embedding Method. - Sai Tej Paruchuri; Jia Guo 004; Andrew Kurdila (CoRR, 2020)

Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels. - Jia Guo 004; Michael E. Kepler; Sai Tej Paruchuri; Haoran Wang; Andrew J. Kurdila; Daniel J. Stilwell (CoRR, 2021)

Collaborative Persistent Excitation in RKHS Embedded Adaptive Estimation with Consensus. - Jia Guo 004; Fumin Zhang 001; Andrew J. Kurdila (ACC, 2022)

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)

Error Bounds for Native Space Embedding Observers with Operator-Valued Kernels. - John A. Burns; Jia Guo 004; Andrew J. Kurdila; Sai Tej Paruchuri; Haoran Wang (ACC, 2023)

Learning Theory Convergence Rates for Observers and Controllers in Native Space Embedding. - John A. Burns; Andrew Kurdila; Derek Oesterheld; Daniel J. Stilwell; Haoran Wang (ACC, 2023)

A Model Reference Adaptive Controller Based on Operator-Valued Kernel Functions. - Derek I. Oesterheld; Daniel J. Stilwell; Andrew J. Kurdila; Jia Guo 004 (CDC, 2023)

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)

Rates of Convergence in Certain Native Spaces of Approximations used in Reinforcement Learning. - Ali Bouland; Shengyuan Niu; Sai Tej Paruchuri; Andrew Kurdila; John A. Burns; Eugenio Schuster (CoRR, 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)

Sufficient Conditions for Parameter Convergence Over Embedded Manifolds Using Kernel Techniques. - Sai Tej Paruchuri; Jia Guo 004; Andrew Kurdila (IEEE Trans. Autom. Control., 2023)

Partial Persistence of Excitation in RKHS Embedded Adaptive Estimation. - Jia Guo 004; Sai Tej Paruchuri; Andrew J. Kurdila (IEEE Trans. Autom. Control., 2023)

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)

Robust, Nonparametric Backstepping Control over Reproducing Kernel Hilbert Spaces. - Haoran Wang; Brian Scurlock; Andrew Kurdila; Andrea L'Afflitto (CDC, 2024)

Nonparametric adaptive control in native spaces: Finite-dimensional implementations, Part II. - Andrew J. Kurdila; Andrea L'Afflitto; John A. Burns; Haoran Wang (Annu. Rev. Control., 2024)

Nonparametric adaptive control in native spaces: A DPS framework (Part I). - Andrew J. Kurdila; Andrea L'Afflitto; John A. Burns; Haoran Wang (Annu. Rev. Control., 2024)

Rates of Convergence in a Class of Native Spaces for Reinforcement Learning and Control. - Ali Bouland; Shengyuan Niu; Sai Tej Paruchuri; Andrew Kurdila; John A. Burns; Eugenio Schuster (IEEE Control. Syst. Lett., 2024)

Convergence Rates of Online Critic Value Function Approximation in Native Spaces. - Shengyuan Niu; Ali Bouland; Haoran Wang; Filippos Fotiadis; Andrew Kurdila; Andrea L'Afflitto; Sai Tej Paruchuri; Kyriakos G. Vamvoudakis (IEEE Control. Syst. Lett., 2024)

Robust Adaptive Backstepping Control Over Native Spaces. - Giorgio A. Orlando; Andrea L'Afflitto; Andrew J. Kurdila (IEEE Control. Syst. Lett., 2025)

Data-Driven, Nonparametric, Adaptive Control Theory - Andrew J. Kurdila; Andrea L'Afflitto; John A. Burns (Springer, 2025)