Results 81 to 90 of about 1,745 (187)

A Koopman-Theoretic Approach to Car-Following and Multi-Lane Interaction Modeling

IEEE Open Journal of Intelligent Transportation Systems
This paper presents a Koopman operator-based approach for the car-following model using SwarmDMD, a dynamic mode decomposition (DMD)-type algorithm designed to capture multi-agent interactions.
Shakib Mustavee, Shaurya Agarwal
doaj   +1 more source

Recursive Forward-Backward EDMD: Guaranteed Algebraic Search for Koopman Invariant Subspaces

IEEE Access
The implementation of the Koopman operator on digital computers often relies on the approximation of its action on finite-dimensional function spaces. This approximation is generally done by orthogonally projecting on the subspace.
Masih Haseli, Jorge Cortes
doaj   +1 more source

Neural Network Augmented Koopman Explicit Model Following Control for Robotic Manipulators

IEEE Access
This paper introduces a Neural Network Augmented Koopman Explicit Model Following Control (NNKEMFC) framework for robust trajectory tracking of robotic manipulators with nonlinear effects and uncertain dynamics.
Oguz Kaan Hancioglu, Mehmet Onder Efe
doaj   +1 more source

Nonlinear System Identification of Tremors Dynamics: A Data-driven Approximation Using Koopman Operator Theory. [PDF]

Int IEEE EMBS Conf Neural Eng, 2023
Xue X, Iyer A, Roque D, Sharma N.
europepmc   +1 more source

Model-Based Control Using Koopman Operators

Robotics: Science and Systems XIII, 2017
This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting model-based control.
Ian Abraham, Gerardo De La Torre, Todd D. Murphey   +2 more
openaire   +2 more sources

Koopman Operator-Based Knowledge-Guided Reinforcement Learning for Safe Human-Robot Interaction. [PDF]

Front Robot AI, 2022
Sinha A, Wang Y.
europepmc   +1 more source

Learning Feature Maps of the Koopman Operator: A Subspace Viewpoint

The Koopman operator was recently shown to be a useful method for nonlinear system identification and controller design. However, the scalability of current data-driven approaches is limited by the selection of feature maps.
Jones, Colin, Lian, Yingzhao
core   +1 more source

Extraction of nonlinearity in neural networks with Koopman operator

Nonlinearity plays a crucial role in deep neural networks. In this paper, we investigate the degree to which the nonlinearity of the neural network is essential.
Sugishita, Naoki, Kinjo, Kayo, Ohkubo, Jun   +2 more
core   +1 more source

Modeling of Nonlinear Dynamical Systems Using Koopman Operator Theory

, 2021
The dynamics of most mechanical systems tends to incorporate nonlinearfunctions and behaviors to model complex systems. Due to the complexity of some of these systems, only analytical solutions can be found to model, optimize and control them however the
Snyder, Gregory Alonzo
core  

The distributional Koopman operator for random dynamical systems

Mathematics of Control, Signals, and Systems
The Distributional Koopman Operator (DKO) is introduced as a way to perform Koopman analysis on random dynamical systems where only aggregate distribution data is available, thereby eliminating the need for particle tracking or detailed trajectory data.
Maria Oprea, Alex Townsend, Yunan Yang
openaire   +2 more sources