Real-time optimal control with shallow recurrent decoder networks
Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor control actions in response to varying scenarios, traditional optimal control problems typically require several system simulations, which are often computationally demanding due to the high-dimensionality of the underlying spatio-temporal dynamics. In this work, we exploit SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize a real-time closed-loop controller for high-dimensional and
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- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
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- LinkedLinked via arxiv author · 85%Matteo Tomasetto →
“Real-time optimal control with shallow recurrent decoder networks”
- LinkedLinked via arxiv author · 85%Francesco Braghin →
“Real-time optimal control with shallow recurrent decoder networks”
- LinkedLinked via arxiv author · 85%J. Nathan Kutz →
“Real-time optimal control with shallow recurrent decoder networks”
- LinkedLinked via arxiv author · 85%Andrea Manzoni →
“Real-time optimal control with shallow recurrent decoder networks”