On analytical construction of observable functions in extended dynamic mode decomposition for nonlinear estimation and prediction
We propose an analytical construction of observable functions in the extended dynamic
mode decomposition (EDMD) algorithm. EDMD is a numerical method for approximating the …
mode decomposition (EDMD) algorithm. EDMD is a numerical method for approximating the …
Graph neural network and Koopman models for learning networked dynamics: A comparative study on power grid transients prediction
Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure
networks, such as the power systems, is a challenging but important task. In particular …
networks, such as the power systems, is a challenging but important task. In particular …
Model predictive optimized path integral strategies
DM Asmar, R Senanayake, S Manuel… - … on Robotics and …, 2023 - ieeexplore.ieee.org
We generalize the derivation of model predictive path integral control (MPPI) to allow for a
single joint distribution across controls in the control sequence. This reformation allows for …
single joint distribution across controls in the control sequence. This reformation allows for …
On computation of koopman operator from sparse data
In this paper, we propose a novel approach to compute the Koopman operator from sparse
time series data. In recent years there have been considerable interests in operator theoretic …
time series data. In recent years there have been considerable interests in operator theoretic …
Koopman operator methods for global phase space exploration of equivariant dynamical systems
In this paper, we develop the Koopman operator theory for dynamical systems with
symmetry. In particular, we investigate how the Koopman operator and eigenfunctions …
symmetry. In particular, we investigate how the Koopman operator and eigenfunctions …
Data-driven operator theoretic methods for global phase space learning
SP Nandanoori, S Sinha… - 2020 American Control …, 2020 - ieeexplore.ieee.org
In this work, we developed new Koopman operator techniques to explore the global phase
space of a nonlinear system from time-series data. In particular, we address the problem of …
space of a nonlinear system from time-series data. In particular, we address the problem of …
Optimal reporter placement in sparsely measured genetic networks using the koopman operator
A Hasnain, N Boddupalli… - 2019 IEEE 58th …, 2019 - ieeexplore.ieee.org
Optimal sensor placement is an important yet unsolved problem in control theory. In
biological organisms, genetic activity is often highly nonlinear, making it difficult to design …
biological organisms, genetic activity is often highly nonlinear, making it difficult to design …
Model predictive control and transfer learning of hybrid systems using lifting linearization applied to cable suspension systems
J Ng, HH Asada - IEEE Robotics and Automation Letters, 2021 - ieeexplore.ieee.org
Model Predictive Control (MPC) of nonlinear hybrid systems using lifting linearization
underpinned by Koopman Operator is presented. Unlike standard linearization, which is …
underpinned by Koopman Operator is presented. Unlike standard linearization, which is …
Generalizing dynamic mode decomposition: Balancing accuracy and expressiveness in Koopman approximations
This paper tackles the data-driven approximation of unknown dynamical systems using
Koopman-operator methods. Given a dictionary of functions, these methods approximate the …
Koopman-operator methods. Given a dictionary of functions, these methods approximate the …
Deep koopman controller synthesis for cyber-resilient market-based frequency regulation
This paper investigates a data-driven countermeasure for price spoofing in the context of
cyber security and market-based frequency regulation. Market-based control of transmission …
cyber security and market-based frequency regulation. Market-based control of transmission …