23 citations · 45 across the 6 of their papers we have counts for
10 papers
Prediction error quantification through probabilistic scaling -- EXTENDED VERSION
Victor Mirasierra, Martina Mammarella, Fabrizio Dabbene +1
In this paper, we address the probabilistic error quantification of a general class of prediction methods. We consider a given prediction model and show how to obtain, through a sa…
Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum
Pablo Krupa, Jose Camara, Ignacio Alvarado +2
This article presents the real-time implementation of the model predictive control for tracking formulation to control a two-wheeled inverted pendulum robot. This formulation offer…
Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
Martina Mammarella, Victor Mirasierra, Matthias Lorenzen +2
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of…
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
Teodoro Alamo, D. G. Reina, Pablo Millán
This document analyzes the role of data-driven methodologies in Covid-19 pandemic. We provide a SWOT analysis and a roadmap that goes from the access to data sources to the final d…
Computationally efficient stochastic MPC: a probabilistic scaling approach
Martina Mammarella, Teodoro Alamo, Fabrizio Dabbene +1
In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, whic…
Open Data Resources for Fighting COVID-19
Teodoro Alamo, Daniel G. Reina, Martina Mammarella +1
We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamen…