2 citations · 2 across the 7 of their papers we have counts for
7 papers
A Comparative Study of MINLP and MPVC Formulations for Solving Complex Nonlinear Decision-Making Problems in Aerospace Applications
Andrea Ghezzi, Armin Nurkanović, Avishai Weiss +2
High-level decision-making for dynamical systems often involves performance and safety specifications that are activated or deactivated depending on conditions related to the syste…
Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
Andrea Ghezzi, Rudolf Reiter, Katrin Baumgärtner +2
We propose a computationally efficient rollout-then-optimize method to improve a learned control policy at deployment time. A learned policy provides a nominal trajectory, which is…
AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Control
Rudolf Reiter, Andrea Ghezzi, Katrin Baumgärtner +3
\Ac{MPC} and \ac{RL} are two powerful control strategies with, arguably, complementary advantages. In this work, we show how actor-critic \ac{RL} techniques can be leveraged to imp…
A Sequential Benders-based Mixed-Integer Quadratic Programming Algorithm and Its Implementation in the CAMINO Toolbox
Andrea Ghezzi, Wim Van Roy, Sebastian Sager +1
Sequential quadratic programming and sequential convex programming efficiently solve nonlinear programs (NLPs) by linearizing inner nonlinearities while preserving the outer convex…
Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation
Andrea Ghezzi, Jasper Hoffman, Jonathan Frey +2
This work presents a novel loss function for learning nonlinear Model Predictive Control policies via Imitation Learning. Standard approaches to Imitation Learning neglect informat…
An Efficient Method for the Joint Estimation of System Parameters and Noise Covariances for Linear Time-Variant Systems
Léo Simpson, Andrea Ghezzi, Jonas Asprion +1
We present an optimization-based method for the joint estimation of system parameters and noise covariances of linear time-variant systems. Given measured data, this method maximiz…
An Implicit and Explicit Dual Model Predictive Control Formulation for a Steel Recycling Process
Andrea Ghezzi, Florian Messerer, Jacopo Balocco +2
We present a formulation for both implicit and explicit dual model predictive control for a steel recycling process. The process consists in the production of new steel by choosing…