2 citations · 3 across the 6 of their papers we have counts for
10 papers
On Convergent Dynamic Mode Decomposition and its Equivalence with Occupation Kernel Regression
Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar
This paper presents a new technique for norm-convergent dynamic mode decomposition of deterministic systems. The developed method utilizes recent results on singular dynamic mode d…
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning
S M Nahid Mahmud, Moad Abudia, Scott A Nivison +2
The objective of this research is to enable safety-critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learn…
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems
S M Nahid Mahmud, Moad Abudia, Scott A Nivison +2
The ability to learn and execute optimal control policies safely is critical to realization of complex autonomy, especially where task restarts are not available and/or the systems…
Singular Dynamic Mode Decompositions
Joel A. Rosenfeld, Rushikesh Kamalapurkar
This manuscript is aimed at addressing several long standing limitations of dynamic mode decompositions in the application of Koopman analysis. Principle among these limitations ar…
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…