Learning · Modeling · Control

Learning-Based Control

This direction explores the broad interface between learning and control. Learning can help represent, model, estimate, predict, and control complex dynamical systems, while systems and control principles provide structure for making these learned components more dependable in closed-loop operation.

Learning-Based Control visual — Image: Zhai et al., Nature Communications (2023), Fig. 2 · CC BY 4.0

Related publications

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Designing neural controllers with optimality and stability guarantees by learning input-output dissipativity thumbnail

Designing neural controllers with optimality and stability guarantees by learning input-output dissipativity

H Wang*, K Miao*, DS Madeira, A Papachristodoulou

Automatica 191, 113120, 2026

arXiv Scholar Published

Learning Koopman Representations with Controllability Guarantees thumbnail

Learning Koopman Representations with Controllability Guarantees

K Miao, H Wang, X Ding, K Gatsis, A Krause, A Papachristodoulou

The Fourteenth International Conference on Learning Representations, 2026

Scholar Published

Opt-ODENet: Neural ODE controller design with differentiable optimization layers for safety and stability thumbnail

Opt-ODENet: Neural ODE controller design with differentiable optimization layers for safety and stability

K Miao, L Zhao, H Wang, K Gatsis, A Papachristodoulou

Proceedings of the 7th Annual Learning for Dynamics and Control Conference, PMLR 283:1217–1229, 2025

PDF PMLR BibTeX Scholar Published

Learning robust state observers using neural odes thumbnail

Learning robust state observers using neural odes

K Miao, K Gatsis

Learning for Dynamics and Control Conference, 208-219, 2023

BibTeX Scholar Published