Sequential decision making in dynamical systems


Rethinking the structure of classic control systems and the metrics used to design them in order to cope with the complexity of modern applications. By modeling the problem as a dynamical system in feedback with an optimization algorithm (i.e. the system dynamics-optimizer plant), we increase the flexibility of the tasks the closed-loop system can accomplish while providing numerically viable solutions for real-time implementations.


We use principles from optimization theory for analysis and design of algorithms in combination with control theoretic tools to account for the dynamics and sequential decision making to capture alternative architectures, controller objectives, and uncertainty descriptions.

Selected publications

  • Karapetyan A., Tsiamis A., Balta E.C., Iannelli A., Lygeros J. - "Implications of Regret on Stability of Linear Dynamical Systems" - IFAC World Congress 2023. Link
  • Karapetyan A., Iannelli A., Lygeros J. - "On the Regret of H control"  - IEEE Conference on Decision and Control 2022. Link
  • Balta E.C., Iannelli A., Smith R.S., Lygeros J. - "Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch" - IEEE Conference on Decision and Control 2022. Link


A. Karapetyan, A. Tsiamis, E.C. Balta, J. Lygeros (ETH Zürich)

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