Antonio Ferramosca

Antonio Ferramosca
University of Bergamo | UNIBG · DIGIP - Department of Managment Industrial and Production Engineering

Ph. D. in Engineering

About

106
Publications
14,661
Reads
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1,389
Citations
Introduction
I received the Bachelor Degree (2004) in Computer Science Engineering, and the Master Degree (2006) in Automation both from the University of Pavia (Italy); and the Ph.D. degree in Automation, Robotics and Telematics, with full marks and honors (summa cum laude), from the University of Seville (Spain) in 2011. My research interests include dynamic systems and control, Model Predictive Control, nonlinear systems, control of biological systems, stability, robust control. My Erdos number is 4.
Additional affiliations
September 2013 - present
National Scientific and Technical Research Council
Position
  • Researcher
April 2012 - August 2013
National Scientific and Technical Research Council
Position
  • PostDoc Position
April 2012 - July 2015
INTEC
Position
  • Assistant Researcher
Education
January 2008 - June 2011
Universidad de Sevilla
Field of study
  • Control Systems Engineering
October 2006 - December 2007
Universidad de Sevilla
Field of study
  • Automation Robotics and Telematics
October 2001 - September 2006
University of Pavia
Field of study
  • Computer Science Engineering

Publications

Publications (106)
Article
The interest in non-linear impulsive systems (NIS) has been growing due to their impact on application problems such as disease treatments (diabetes, HIV, influenza, COVID-19, among many others), where the control action (drug administration) is given by short-duration pulses followed by time periods of null values. Within this framework, the conce...
Preprint
Full-text available
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 least amount of function evaluations or sample comparisons as possible. In the black-box case, the analytical expression of the objective function is unknown and it can o...
Preprint
Full-text available
Preference-based optimization algorithms are iterative procedures that seek the optimal value for a decision variable based only on comparisons between couples of different samples. At each iteration, a human decision-maker is asked to express a preference between two samples, highlighting which one, if any, is better than the other. The optimizati...
Chapter
Several mathematical models in SARS-CoV-2 have shown how the target cell model can help to understand the spread of the virus in the host and how potential antiviral treatments can help to control the virus. Concepts as equilibrium and stability have shown to be crucial to qualitatively determine the best alternatives to schedule drugs, based on th...
Article
Full-text available
Recent developments in machine learning applications are deeply concerned with the poor interpretability of most of these techniques. To gain some insights in the process of designing data-based models it is common to graphically represent the algorithm’s results, either in their final or intermediate stage. Specially challenging is the task of plo...
Article
The main contribution of this article is to provide the key concept—from a control point of view—of a region of the state space called cyclic control equilibria, which is a permanence region for switched systems under arbitrary waiting-time constraints. The study also includes a discussion about the typical permanence regions (steady-states, multip...
Article
A coalitional robust model predictive controller for tracking target sets is presented. The overall system is controlled by a set of local control agents that dynamically merge into cooperative coalitions or clusters so as to attain an efficient trade-off between cooperation burden and global performance optimality. Within each cluster, the agents...
Preprint
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 recently developed to minimize either the infected peak prevalence (IPP) or the epidemic final size (EFS). While most of the control strategies optimize a simple cost fu...
Preprint
Full-text available
Several mathematical models in SARS-CoV-2 have shown how target-cell model can help to understand the spread of the virus in the host and how potential candidates of antiviral treatments can help to control the virus. Concepts as equilibrium and stability show to be crucial to qualitatively determine the best alternatives to schedule drugs, accordi...
Conference Paper
This work proposes a single-layer finite-horizon optimal control strategy to solve the autonomous navigation problem while accounting for energy efficiency and providing obstacle avoidance feature in cluttered environments with unknown obstacles. Considering the rate capacity effect of electric batteries, the nonlinear state-of-charge behavior is d...
Article
This paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce the next topology. Accordingly, agents can predict...
Preprint
Social distancing strategies have been adopted by governments to manage the COVID-19 pandemic, since the first outbreak began. However, further epidemic waves keep out the return of economic and social activities to their standard levels of intensity. Social distancing interventions based on control theory are needed to consider a formal dynamic ch...
Article
Full-text available
In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in this work simultaneously provides an optimizing solu...
Article
Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a tre...
Preprint
Full-text available
Although modeling studies are focused on the control of SIR-based systems describing epidemic data sets (particularly the COVID-19), few of them present a formal dynamic characterization in terms of equilibrium sets and stability. Such concepts can be crucial to understand not only how the virus spreads in a population, but also how to tailor gover...
Article
Social distancing strategies have been adopted by governments to manage the COVID-19 pandemic, since the first outbreak began. However, further epidemic waves keep out the return of economic and social activities to their standard levels of intensity. Social distancing interventions based on control theory are needed to consider a formal dynamic ch...
Preprint
Full-text available
Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a tre...
Conference Paper
Full-text available
This paper deals with the trajectory tracking problem of a tilt-rotor unmanned aerial vehicle carrying a suspended load. An explicit model predictive control (eMPC) based on multiparametric optimization is used to derive optimal control laws which could be implemented in an embedded system. The eMPC is designed based on the nominal linearized error...
Preprint
The interest in non-linear impulsive systems (NIS) has been growing due to its impact in application problems such as disease treatments (diabetes, HIV, influenza, among many others), where the control action (drug administration) is given by short-duration pulses followed by time periods of null values. Within this framework the concept of equilib...
Article
This paper studies switched systems in which the manipulated control action is the time-depending switching signal. To control the switched systems means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often diffi...
Article
While many epidemiological models were proposed to understand and handle COVID-19 pandemic, too little has been invested to understand human viral replication and the potential use of novel antivirals to tackle the infection. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are characterized....
Article
En este articulo se propone una estrategia de dise~no para sistemas de control de flujos de energ´ıa en microrredes el´ectricas con generaci´on renovables, aplicando el Control Predictivo Econ´omico basado en Modelo (EMPC).El modelo de microrred utilizado se compone por un sistema de almacenamiento, una fuente de generaci´on renovable, un perfil de...
Article
This work presents a pulsatile Zone Model Predictive Control (pZMPC) for the control of blood glucose concentration (BGC) in patients with Type 1 Diabetes Mellitus (T1DM). The main novelties of the algorithm – in contrast to other existing strategies – are: (i) it controls the patient glycemia by injecting short duration insulin boluses for both, t...
Article
In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied by means of an impulsive zone model predictive control (iZMPC), which bases its predictions on a novel long‐term glucose‐insulin model. Taking advantage of the impulsive version of the model—which features real‐life properties of diabetes patient...
Preprint
Switched systems in which the manipulated control action is the time-depending switching signal describe many engineering problems, mainly related to biomedical applications. In such a context, to control the system means to select an autonomous system - at each time step - among a given finite family. Even when this selection can be done by solvin...
Preprint
While many epidemiological models have being proposed to understand and handle COVID-19, too little has been invested to understand how the virus replicates in the human body and potential antiviral can be used to control the replication cycle. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans...
Article
Many engineering applications can be described as switched linear systems, in which the manipulated control action is the time-dependent switching signal. In such a case, the control strategy must select a linear autonomous system at each time step, among a finite number of them. Even when this selection can be done by solving a Dynamic Programming...
Article
In this paper, we address the problem of modeling error in economically optimal control. A single layer controller is proposed that integrates the economical part of the Real Time Optimization (RTO), the dynamic part of the Model Predictive Control (MPC) and the Modifier Adaptation strategy (MA), resulting in a controller with the following charact...
Conference Paper
Full-text available
This work presents a Nonlinear Model Predictive Control strategy for a quadrotor UAV with obstacle avoidance capability in a 3D unknown environment with static obstacles. The system aims to reach the target in minimum time while avoiding obstacles and also to take into account the energy of states and inputs. Sensor information is processed to dete...
Conference Paper
Full-text available
This work presents a Nonlinear Model Predictive Control strategy for mobile robot navigation in unknown environments. The control system aims to reach a goal safely, as fast as possible, minimizing the control effort, and the distance between the current trajectory and the goal. A LIDAR (Light Detection and Ranging) sensor is used to obtain obstacl...
Preprint
Full-text available
This paper presents a novel set-based model predictive control for tracking, with the largest domain of attraction. The formulation - which consists of a single optimization problem - shows a dual behavior: one operating inside the maximal controllable set to the feasible equilibrium set, and the other operating at the $N$-controllable set to the s...
Article
This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any changing setpoints and enlarged domain of attraction,...
Article
This work extends a recent set-based Model Predictive Control (MPC) scheme for closed loop re-identification that solves the potential conflict between the simultaneous persistent excitation of the system and the stabilization of the closed-loop system. Based on the original scheme proposed in González et al. (2014), this manuscript extends those r...
Article
This note presents a robust economic model predictive control controller suitable for changing economic criterion. The proposal ensures feasibility under any change of the economic criterion, thanks to the use of artificial variables and a relaxed terminal constraint, and robustness in presence of additive bounded disturbances. The resulting robust...
Article
Robust asymptotic stability (asymptotic attractivity and ϵ‐δ stability) of equilibrium regions under robust model predictive control (MPC) strategies was extensively studied in the last decades making use of Lyapunov theory in most cases. However, in spite of its potential application benefits, the problem of finite‐time convergence under fixed pre...
Article
This paper presents a two-stage cascade control framework to solve hierarchically the trajectory tracking problem of a Tilt-rotor Unmanned Aerial Vehicle (UAV) carrying a suspended load. Initially, a nonlinear dynamic model is presented, which is after decoupled into two subsystems. The outer control system is designed by means of a robust tube-bas...
Article
Here, the implementation of the gradient-based Economic MPC (Model Predictive Control) in an industrial distillation system is studied. The approach is an alternative to overcome the conflict between the MPC and RTO (Real Time Optimization) layers in the conventional control structure. The study is based on the rigorous dynamic simulation software...
Article
Full-text available
This work proposes a control strategy to solve the path tracking problem of a suspended load carried by a tilt-rotor unmanned aerial vehicle (UAV). Initially, the equations of motion for the multibody mechanical system are derived from the load’s perspective by means of the Euler-Lagrange formulation, in which the load’s position and orientation ar...
Article
One of the main reported problems in petrochemical applications of Linear Programming-Dynamic Matrix Control (LP-DMC) type controllers is their global performance assessment. Since the stationary optimization and dynamic control blocks have not a transparent link between them, it is not easy to find appropriate references to evaluate the overall pe...
Article
In this work the problem of regulating glycemia in type I diabetic patients is studied by means of an impulsive zone model predictive control (impulsive ZMPC) based on a novel long-term glucose-insulin model. Taking advantage of the model - which features real life properties of diabetes patients that some other popular models do not - the proposed...
Article
Economic Model Predictive Controllers, consisting of an economic criterion as stage cost for the dynamic regulation problem, have shown to improve the economic performance of the controlled plant. However, throughout the operation of the plant, if the economic criterion changes – due to variations of prices, costs, production demand, market fluctua...
Article
A gradient-based model predictive control (MPC) strategy was recently proposed to reduce the computational burden derived from the explicit inclusion of an economic real time optimization (RTO). The main idea is to compute a suboptimal solution, which is the convex combination of a feasible solution and a solution of an approximated (linearized) pr...
Conference Paper
This work presents a new methodology for tuning industrial predictive controllers, with the aim to fit the parameters of the (associated) stationary optimization problem - which decides the optimal economic operation. A step by step procedure to find the right tuning values are provided, and a case study taken from a real natural gas processing uni...
Conference Paper
In this work the problem of regulating glycemia in type I diabetic patients is studied by means of a novel impulsive zone model predictive control. According to the control objective of steering the system to an arbitrary desired target set, weak stability is demonstrated based on a novel dynamic characterization of two underlying discrete-time sub...
Conference Paper
This note presents a robust economic MPC controller, suitable for changing economic criterion. The proposal ensures feasibility under any change of the economic criterion - thanks to the use of artificial variables and a relaxed terminal constraint - and robustness in presence of additive bounded disturbances, by means of nominal predictions and re...
Article
Recently, a linear Model Predictive Control (MPC) suitable for closed-loop re-identification was proposed, which solves the potential conflict between the persistent excitation of the system (necessary to perform a suitable identification) and the control, and guarantees recursive feasibility and attractivity of an invariant region of the closed-lo...
Article
Full-text available
Recently, a Model Predictive Control (MPC) suitable for closed-loop re-identification was proposed, which solves the potential conflict between the persistent excitation of the system and the stabilization of the closed-loop by extending the equilibrium-point-stability to the invariant-set-stability. The proposed objective set, however, derives in...
Conference Paper
Full-text available
Recently, a Model Predictive Control (MPC) suitable for closed-loop re-identification was proposed ([1]), which solves the potential conflict between the persistent excitation of the system-necessary to perform a suitable identification-and the stabilization of the closed-loop. The novel idea consists in extending the concept of equilibrium-point-s...
Conference Paper
Full-text available
Recently, a gradient-based model pre-dictive control (MPC) strategy was proposed to reduce the computational burden of integrating real time optimization (RTO) and control: the main idea is to obtain the on-line controller solution by means of the convex combination of a feasible solution and a solution of an approximated (linearized) problem. This...
Conference Paper
Full-text available
In this paper a zone MPC controller is proposed to deal with the tracking problem of linear impulsive control systems. The control strategy is based on the analysis of some system equilibrium generalizations, which are characterized by means of two underlying discrete-time systems. First, it is shown that the impulsive system has a kind of orbits i...
Article
Full-text available
Model Predictive Control (MPC) is one of the most used advanced control strategy in the industries, mainly due to its capability to fulfill economic objectives, taking into account a simplified dynamic model of the plant, constraints, and stability requirements. In the last years, several economic formulations of MPC have been presented, which over...
Article
In this chapter, a cooperative distributed MPC is presented. The main features of this control strategy are: constraints satisfaction; cooperation between agents to achieve an agreement; closed-loop stability that is always ensured, even in the case of just one iteration; achieved control actions that are plantwide Pareto optimal and equivalent to...
Article
Full-text available
Economic Model Predictive Controllers, consisting of an economic criterion as stage cost for the dynamic regulation problem, have shown to improve the economic performance of the controlled plant, as well as to ensure stability of the economic setpoint. However, throughout the operation of the plant, economic criteria are usually subject to frequen...
Article
Model Predictive Control (MPC) is the most used advanced control strategy in the industries, mainly due to its capability to fulfill economic objectives, taking into account a dynamic simplified model of the plant, constraints, and stability requirements. In the last years, several economic formulations of MPC have been presented, which get over th...