Formulating analysis and synthesis methods to better understand fundamental limitations and robustness properties of closed-loop systems featuring learning components (e.g. parameter estimator, data-driven controller) and propose improved solutions. We would like to contribute to the understanding of when and why a learning-based controller is preferable to more standard solutions and what are the properties that such a controller should have.
We combine standard subjects in the field of systems theory such as adaptive control and robust control with methodologies from reinforcement learning and information theory.
- Parsi A., Iannelli A., Smith R.S. - "An explicit dual control approach for constrained reference tracking of uncertain linear systems" - IEEE Transactions on Automatic Control (special issue: Learning for Control), 2022. Link
- Yin M., Iannelli A., Smith R.S. - "Maximum Likelihood Estimation in Data-Driven Modeling and Control" - IEEE Transactions on Automatic Control, 2021. Link
- Iannelli A., Smith R.S. - "A multiobjective LQR synthesis approach to dual control for uncertain plants" - IEEE Control Systems Letters, vol. 4, 2020. Link
- Yin M., Iannelli A., Smith R.S. - "Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates" - European Control Conference 2022. Link
- Scampicchio A., Iannelli A. - "An update-and-design scheme for scenario-based LQR synthesis" - American Control Conference 2022. Link
- Yin M., Iannelli A., Smith R.S. - "Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control" - Learning For Dynamics and Control Conference 2021. Link
M. Yin, A. Parsi, R. S. Smith (ETH Zürich)