9 citations · 9 across the 5 of their papers we have counts for
5 papers
Data-driven Koopman Operator-based Prediction and Control Using Model Averaging
Daisuke Uchida, Karthik Duraisamy
This work presents a data-driven Koopman operator-based modeling method using a model averaging technique. While the Koopman operator has been used for data-driven modeling and con…
Model Predictive Control of Nonlinear Dynamics Using Online Adaptive Koopman Operators
Daisuke Uchida, Karthik Duraisamy
This paper develops a methodology for adaptive data-driven Model Predictive Control (MPC) using Koopman operators. While MPC is ubiquitous in various fields of engineering, the con…
Extracting Koopman Operators for Prediction and Control of Non-linear Dynamics Using Two-stage Learning and Oblique Projections
Daisuke Uchida, Karthik Duraisamy
The Koopman operator framework provides a perspective that non-linear dynamics can be described through the lens of linear operators acting on function spaces. As the framework nat…
Control-aware Learning of Koopman Embedding Models
Daisuke Uchida, Karthik Duraisamy
A learning method is proposed for Koopman operator-based models with the goal of improving closed-loop control behavior. A neural network-based approach is used to discover a space…
Data-Driven Koopman Controller Synthesis Based on the Extended Norm Characterization
Daisuke Uchida, Atsushi Yamashita, Hajime Asama
This paper presents a new data-driven controller synthesis based on the Koopman operator and the extended norm characterization of discrete-time linear systems. We…