1 citations · 1 across the 12 of their papers we have counts for
12 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…
Iterative Learning Predictive Control for Constrained Uncertain Systems
Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan +1
Iterative learning control (ILC) improves the performance of a repetitive system by learning from previous trials. ILC can be combined with Model Predictive Control (MPC) to mitiga…
Data-Enabled Predictive Iterative Control
Kai Zhang, Riccardo Zuliani, Efe C. Balta +1
This work introduces the Data-Enabled Predictive iteRative Control (DeePRC) algorithm, a direct data-driven approach for iterative LTI systems. The DeePRC learns from previous iter…
Closed-loop Performance Optimization of Model Predictive Control with Robustness Guarantees
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model mismatch and process noise are two frequently occurring phenomena that can drastically affect the performance of model predictive control (MPC) in practical applications. We…
BP-MPC: Optimizing the Closed-Loop Performance of MPC using BackPropagation
Riccardo Zuliani, Efe C. Balta, John Lygeros
Model predictive control (MPC) is pervasive in research and industry. However, designing the cost function and the constraints of the MPC to maximize closed-loop performance remain…
Convergence guarantees for adaptive model predictive control with kinky inference
Riccardo Zuliani, Raffaele Soloperto, John Lygeros
We analyze the convergence properties of a robust adaptive model predictive control algorithm used to control an unknown nonlinear system. We show that by employing a standard quad…
Batch Model Predictive Control for Selective Laser Melting
Riccardo Zuliani, Efe C. Balta, Alisa Rupenyan +1
Selective laser melting is a promising additive manufacturing technology enabling the fabrication of highly customizable products. A major challenge in selective laser melting is e…