3 citations · 6 across the 15 of their papers we have counts for
5 papers · 1 filter
Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
In this paper, we address Linear Quadratic Regulator (LQR) problems through a novel iterative algorithm named EXtremum-seeking Policy iteration LQR (EXP-LQR). The peculiarity of EX…
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…
Towards Parameter-free Distributed Optimization: a Port-Hamiltonian Approach
Rodrigo Aldana-López, Alessandro Macchelli, Giuseppe Notarstefano +2
This paper introduces a novel distributed optimization technique for networked systems, which removes the dependency on specific parameter choices, notably the learning rate. Tradi…
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 Unifying System Theory Framework for Distributed Optimization and Games
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
This paper introduces a systematic methodological framework to design and analyze distributed algorithms for optimization and games over networks. Starting from a centralized metho…