2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
A Dynamic Mode Decomposition Approach to Parameter Identification
Moad Abudia, Opeyemi Owolabi, Joel A. Rosenfeld +1
This paper presents a data-driven algorithm for simultaneous system identification and parameter estimation in control-affine nonlinear systems. Parameter estimation is achieved by…
Dynamic Mode Decomposition of Control-Affine Nonlinear Systems using Discrete Control Liouville Operators
Zachary Morrison, Moad Abudia, Joel Rosenfeld +1
Representation of nonlinear dynamical systems as infinite-dimensional linear operators over Hilbert spaces enables analysis of nonlinear systems via pseudo-spectral operator analys…
An occupation kernel approach to optimal control
Rushikesh Kamalapurkar, Joel A. Rosenfeld
In this effort, a novel operator theoretic framework is developed for data-driven solution of optimal control problems. The developed methods focus on the use of trajectories (i.e.…
Motion Tomography via Occupation Kernels
Benjamin P. Russo, Rushikesh Kamalapurkar, Dongsik Chang +1
The goal of motion tomography is to recover a description of a vector flow field using information on the trajectory of a sensing unit. In this paper, we develop a predictor correc…
Theoretical Foundations for the Dynamic Mode Decomposition of High Order Dynamical Systems
Joel A. Rosenfeld, Benjamin P. Russo, Rushikesh Kamalapurkar
Conventionally, data driven identification and control problems for higher order dynamical systems are solved by augmenting the system state by the derivatives of the output to for…
The Gradient descent method from the perspective of fractional calculus
Pham Viet Hai, Joel A. Rosenfeld
Motivated by gradient methods in optimization theory, we give methods based on -fractional derivatives of order in order to solve unconstrained optimization problems. The co…