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Distributed Koopman operator learning from sequential observations
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DOI：10.1016/j.ejcon.2026.101497.png)
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Abstract
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This paper presents a distributed Koopman operator learning framework for modeling unknown nonlinear dynamics using sequential observations from multiple agents. Each agent estimates a local Koopman approximation based on lifted data and collaborates over a communication graph to reach exponential consensus on a consistent distributed approximation. The approach supports distributed computation under asynchronous and resource-constrained sensing. Its performance is demonstrated through simulation results, validating convergence and predictive accuracy under sensing-constrained scenarios and limited communication.
Keywords:
Koopman operator
Distributed learning
Multi-agent systems
Nonlinear system identification
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