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 parametric dynamics, relying solely on limited state sensor readings. After training the model o...
| Authors | Matteo Tomasetto, Francesco Braghin, J. Nathan Kutz et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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