25 citations · 44 across the 12 of their papers we have counts for
14 papers
Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation
Pablo Krupa, Johannes Köhler, Antonio Ferramosca +4
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artifici…
Model Predictive Control for setpoint tracking
Daniel Limon, Antonio Ferramosca, Ignacio Alvarado +1
The main objective of tracking control is to steer the tracking error, that is the difference between the reference and the output, to zero while the plant's operation limits are s…
Set-Point Tracking MPC with Avoidance Features
Marcelo A. Santos, Antonio Ferramosca, Guilherme V. Raffo
This work proposes a finite-horizon optimal control strategy to solve the tracking problem while providing avoidance features to the closed-loop system. Inspired by the set-point t…
A unified surrogate-based scheme for black-box and preference-based optimization
Davide Previtali, Mirko Mazzoleni, Antonio Ferramosca +1
Black-box and preference-based optimization algorithms are global optimization procedures that aim to find the global solutions of an optimization problem using, respectively, the…
GLISp-r: A preference-based optimization algorithm with convergence guarantees
Davide Previtali, Mirko Mazzoleni, Antonio Ferramosca +1
Preference-based optimization algorithms are iterative procedures that seek the optimal calibration of a decision vector based only on comparisons between couples of different tuni…
Minimizing the Epidemic Final Size while Containing the Infected Peak Prevalence in SIR Systems
J. Sereno, A. L. Anderson, A. Ferramosca +2
Mathematical models are instrumental to forecast the spread of pathogens and to evaluate the effectiveness of non-pharmaceutical measures. A plethora of optimal strategies has been…