Results 41 to 50 of about 906 (226)
Koopman Operator Theory and The Applied Perspective of Modern Data-Driven Systems [PDF]
, 2022 Recent theoretical developments in dynamical systems and machine learning have allowed researchers to re-evaluate how dynamical systems are modeled and controlled.
Krolicki, Alex
core
Koopman Operator Theory for Nonlinear Dynamic Modeling using Dynamic Mode Decomposition
CoRR, 2021 8 pages, 16 ...
Gregory Snyder, Zhuoyuan Song
openaire +2 more sources
Data-Driven Newton Raphson Controller Based on Koopman Operator Theory
CoRR, 2023 Newton-Raphson controller is a powerful prediction-based variable gain integral controller. Basically, the classical model-based Newton-Raphson controller requires two elements: the prediction of the system output and the derivative of the predicted output with respect to the control input.
openaire +2 more sources
Inverted Gaussian Process Optimization for Probabilistic Koopman Operator Discovery
IEEE AccessKoopman Operator theory opens the door for application of rich linear systems theory to data-driven modeling and control of nonlinear dynamic systems.
Abhigyan Majumdar +2 more
doaj +1 more source
Reduced-order modelling based on Koopman operator theory
CoRRThe present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output using Koopman operator theory. A reduced-order modelling method that incorporates a novel strategy for identifying the most impactful Koopman modes was used to numerically ...
Diana Alina Bistrian +2 more
openaire +2 more sources
IEEE Access, 2022 This paper proposes a data-centric model predictive control (MPC) for supplemental control of a DFIG-based wind farm (WF) to improve power system stability.
Ahmed Husham +3 more
doaj +1 more source
Nonlinear System Identification of Soft Robot Dynamics Using Koopman Operator Theory [PDF]
2019 International Conference on Robotics and Automation (ICRA), 2019 Soft robots are challenging to model due in large part to the nonlinear properties of soft materials. Fortunately, this softness makes it possible to safely observe their behavior under random control inputs, making them amenable to large-scale data collection and system identification. This paper implements and evaluates a system identification method
Daniel Bruder +2 more
openaire +2 more sources
Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 more
wiley +1 more source
Randomized Projection Learning Method for Dynamic Mode Decomposition
Mathematics, 2021 A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced ...
Sudam Surasinghe, Erik M. Bollt
doaj +1 more source
Does Participating in Agricultural Global Value Chains Promote Agricultural Growth?
Agribusiness, EarlyView.ABSTRACT This study examines the relationship between GVC participation and agricultural value‐added growth in 43 countries over the period 1995–2022. In contrast to prior literature, we disaggregate the agricultural sector into four sub‐sectors namely crop cultivation, animal production, forestry and fishing.
Taner Turan +2 more
wiley +1 more source