Distributed Koopman Learning with Incomplete Measurements Article Swipe
Wenjian Hao
,
Lili Wang
,
Ayush Rai
,
Shaoshuai Mou
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.11586
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.11586
Koopman operator theory has emerged as a powerful tool for system identification, particularly for approximating nonlinear time-invariant systems (NTIS). This paper considers a network of agents with limited observation capabilities that collaboratively estimate the dynamics of an NTIS. A distributed deep Koopman learning algorithm is developed by integrating Koopman operator theory, deep neural networks, and consensus-based coordination. In the proposed framework, each agent approximates the system dynamics using its partial measurements and lifted states exchanged with its neighbors. This cooperative scheme enables accurate reconstruction of the global dynamics despite the absence of full-state information at individual agents. Simulation results on the Lunar Lander environment from OpenAI Gym demonstrate that the proposed method achieves performance comparable to the centralized deep Koopman learning with full-state access.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.11586
- https://arxiv.org/pdf/2409.11586
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403713748
All OpenAlex metadata
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https://openalex.org/W4403713748Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.11586Digital Object Identifier
- Title
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Distributed Koopman Learning with Incomplete MeasurementsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-09-17Full publication date if available
- Authors
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Wenjian Hao, Lili Wang, Ayush Rai, Shaoshuai MouList of authors in order
- Landing page
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https://arxiv.org/abs/2409.11586Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2409.11586Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.11586Direct OA link when available
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Dynamics (music), Nonlinear system, Computer science, Deep learning, Artificial intelligence, Statistical physics, Psychology, Physics, Pedagogy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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