7 papers
Model-Agnostic Meta Learning for Differentiable MPC
Salma Elfeki, Riccardo Zuliani, Niklas Schmid +2
Applying policy optimization to Model Predictive Control (MPC) yields high-performance and reliable controllers. However, the resulting controllers often overfit their training con…
Multi-scale closed-loop melt pool control for LPBF via policy optimization
Junan Lin, Riccardo Zuliani, Baris Kavas +3
Laser powder bed fusion (LPBF) is a metal additive manufacturing process where temperature stabilization is of vital importance to avoid defects such as distortion and cracking. Ex…
Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model-based policy optimization is a well-established framework for designing reliable and high-performance controllers across a wide range of control applications. Recently, this…
Policy Optimization for Unknown Systems using Differentiable Model Predictive Control
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model-based policy optimization often struggles with inaccurate system dynamics models, leading to suboptimal closed-loop performance. This challenge is especially evident in Model…
Loss-aware distributionally robust optimization via trainable optimal transport ambiguity sets
Jonas Ohnemus, Marta Fochesato, Riccardo Zuliani +1
Optimal-Transport Distributionally Robust Optimization (OT-DRO) robustifies data-driven decision-making under uncertainty by capturing the sampling-induced statistical error via op…
Differentiable-by-design Nonlinear Optimization for Model Predictive Control
Riccardo Zuliani, Efe C. Balta, John Lygeros
Nonlinear optimization-based control policies, such as those those arising in nonlinear Model Predictive Control, have seen remarkable success in recent years. These policies requi…