Data-driven Distributed Learning of Multi-agent Systems: A Koopman Operator Approach
- BATTELLE (PACIFIC NW LAB)
Koopman operator theory provides a model-free technique for studying nonlinear dynamical systems purely from data. Since the Koopman operator is infinite-dimensional, researchers have developed several methods that provide a finite-dimensional approximation of the Koopman operator so that it can be applied for practical use cases. One common thing with most of the methods is that their solutions are obtained by solving a centralized minimization problem. In this work, we treat the dynamical system to be a multi-agent system and propose an algorithm to compute the finite-dimensional approximation of the Koopman operator in a distributed manner using the knowledge of the topology of the underlying multi-agent system. The proposed distributed approach is shown to be equivalent to the centralized learning problem and results in a sparse Koopman whose block structure mimics the Laplacian of the multi-agent system. Extensive simulation studies illustrate the proposed framework on the network of oscillators and the IEEE 68 bus system.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1862317
- Report Number(s):
- PNNL-SA-160981
- Country of Publication:
- United States
- Language:
- English
Similar Records
Data-driven modeling and control of dynamical systems using Koopman and Perron-Frobenius operators
Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems
Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach
Other
·
Fri Sep 04 00:00:00 EDT 2020
·
OSTI ID:1693451
Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems
Conference
·
Thu Dec 29 23:00:00 EST 2022
·
OSTI ID:2000514
Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach
Conference
·
Tue Nov 10 23:00:00 EST 2020
·
OSTI ID:1763306