Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation

no code implementations • 10 Jan 2024 • Carmine Caponio, Pietro Stano, Raffaele Carli, Ignazio Olivieri, Daniele Ragone, Aldo Sorniotti, Umberto Montanaro

The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm.

Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy

no code implementations • 9 Nov 2020 • Paolo Scarabaggio, Massimo La Scala, Raffaele Carli, Mariagrazia Dotoli

To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i. e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency.