23 citations · 67 across the 16 of their papers we have counts for
8 papers · 1 filter
Probabilistic interval predictor based on dissimilarity functions
A. Daniel Carnerero, Daniel R. Ramirez, Teodoro Alamo
This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the…
Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm
Pablo Krupa, Ignacio Alvarado, Daniel Limon +1
This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded system…
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…
Probabilistic reachable and invariant sets for linear systems with correlated disturbance
Mirko Fiacchini, Teodoro Alamo
In this paper a constructive method to determine and compute probabilistic reachable and invariant sets for linear discrete-time systems, excited by a stochastic disturbance, is pr…