Developing algorithms for the identification of mathematical models describing the evolution of dynamical systems which are difficult to model only using first principles. We are also interested in considering the problem of quantifying the uncertainty associated with the estimated models and providing systematic means for introducing prior information in the identification.
We build our approaches on established subjects in the field of systems theory such as system identification and estimation and we foster their integration with techniques from the related fields of statistical learning theory and kernel-based methods.
- Mehrjou A., Iannelli A., Schölkopf B. - "Learning Dynamical Systems using Local Stability Priors" - Journal of Computational Dynamics (special issue: Computation of Lyapunov functions and contraction metrics), vol. 10, 2022. Link
- Iannelli A., Fasel U., Smith R.S. - "The Balanced Mode Decomposition Algorithm for Data-Driven LPV Low-Order Models of Aeroservoelastic Systems" - Aerospace Science and Technology, vol. 115, 2021. Link
- Yin M., Iannelli A., Smith R.S. - "Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs" - IEEE Control Systems Letters, vol. 5, 2021. Link
- Ozan D.E., Yin M., Iannelli A., Smith R.S. - "Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models" - IEEE Conference on Decision and Control 2022. Link
- Iannelli A., Yin M., Smith R.S. - "Experiment design for impulse response identification with signal matrix models" - IFAC Symposium on System Identification 2021. Link
- Iannelli A., Smith R.S. - "The role of the state in model reduction with subspace and POD-based data-driven methods" - American Control Conference 2021. Link
- Khosravi M., Iannelli A., Yin M., Parsi A., Smith R.S. - "Regularized System Identification: A Hierarchical Bayesian Approach" - IFAC World Congress 2020. Link
- Khosravi M., Yin M., Iannelli A., Parsi A., Smith R.S. - "Low-Complexity Identification by Sparse Hyperparameter Estimation" - IFAC World Congress 2020. Link
M. Yin, R. S. Smith (ETH Zürich), M. Khosravi (TU Delft).