5 papers
DATA-DRIVEN PRONTO: a Model-free Solution for Numerical Optimal Control
Marco Borghesi, Lorenzo Sforni, Giuseppe Notarstefano
This article addresses the problem of data-driven numerical optimal control for unknown nonlinear systems. In our scenario, we suppose to have the possibility of performing multipl…
Multi-Robot Target Monitoring and Encirclement via Triggered Distributed Feedback Optimization
Lorenzo Pichierri, Guido Carnevale, Lorenzo Sforni +1
We design a distributed feedback optimization strategy, embedded into a modular ROS 2 control architecture, which allows a team of heterogeneous robots to cooperatively monitor and…
Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
Lorenzo Sforni, Guido Carnevale, Ivano Notarnicola +1
In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing st…
A Distributed Online Optimization Strategy for Cooperative Robotic Surveillance
Lorenzo Pichierri, Guido Carnevale, Lorenzo Sforni +2
In this paper, we propose a distributed algorithm to control a team of cooperating robots aiming to protect a target from a set of intruders. Specifically, we model the strategy of…
GoPRONTO: a Feedback-based Framework for Nonlinear Optimal Control
Lorenzo Sforni, Sara Spedicato, Ivano Notarnicola +1
In this paper we We propose GoPRONTO, a first-order, feedback-based approach to solve nonlinear discrete-time optimal control problems. This method is a generalized first-order fra…