Antonio Ferramosca

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

Advanced hybrid closed loop (AHCL) systems currently represent the most advanced modality of insulin therapy. To compare the night-time (from 00 to 07 a.m.) effectiveness in achieving recommended glycemic targets of three different AHCL systems in adults with type 1 diabetes (T1D). We retrospectively evaluated 55 adults with T1D (mean age 41 ± 16 y...

In this paper, we present a robust control strategy based on model predictive control (MPC) for a tiltrotor unmanned aerial vehicle in suspended load transportation tasks, from the perspective of the load. To this aim, we propose a new formulation of tube-based MPC for high-order dynamical systems, allowing its embedded implementation. The proposed...

Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an...

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 artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, inclu...

Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an...

This paper presents a novel pulsatile Zone Model Predictive Control (pZMPC) for glycemic control in type 1 diabetic patients, which is an extension of the one presented in literature. Its main characteristics are (i) the explicit inclusion of a time-varying insulin on board constraint to promote a non-zero insulin delivery after a standard bolus in...

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 tracking model predictive control (MPC) framework, the central idea of including artificial variables into the optimal control problem is considered. This approach allows...

In this article a methodology, based on the Kano model, to prioritize the features of a product or service is proposed. Instead of using detailed, lengthy, burdensome, time-demanding, and biased-prone questionnaires, enquiring the user satisfaction with each product feature, a simplified survey asking for the overall satisfaction with the product i...

This work introduces a novel zone model predictive control (MPC) based on Gaussian Process models (GPs) for the artificial pancreas (AP). The main novelty of the proposal is to exploit a GP that is trained on previously collected metabolic data of type 1 diabetes mellitus (T1DM) patients, to regulate the blood glucose levels by means of a personali...

Predictive maintenance (PdM) is a set of actions and techniques to early detect failures and defects on machines before they occur, and the usage of machine learning and deep learning techniques in predictive maintenance has increased during the last years. Even with this increase of the literature, there is still a gap concerning the application o...

This work proposes single-layer nonlinear model predictive control schemes to solve the autonomous navigation problem while providing obstacle avoidance feature in cluttered environments with previously unknown obstacles. Considering model predictive control frameworks for set-point stabilization and set-point tracking, the penalty method of nonlin...

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 tracking model predictive control (MPC) framework, the central idea of including artificial variables into the optimal control problem is considered. This approach allows...

Currently, there has been a sharp increase in epidemic control research as a result of recent epidemic outbreaks. Several strategies aiming to minimize the Epidemic Final Size and/or to keep the Infected Peak Prevalence under a specific value were proposed. However, not many strategies focused on analyzing the impact of applying quantified measures...

Recurrent Neural Networks (RNN) of the Long Short Term Memory (LSTM) type provide high accuracy in predicting sequential models in various application domains. As in most process control problems, their dynamics include non-manipulated variables that need to be predicted. This paper proposes using an LSTM neural network for energy demand forecastin...

The deployment of microgrids connected to an electricity grid is increasing every day. These energy districts with their control system are the intelligent nodes of future electricity grids; therefore, strategies for managing these new systems must be developed and proposed. This paper presents a novel coalitional economic model predictive control...

Preference-based optimization algorithms are iterative procedures that seek the optimal calibration of a decision vector based only on comparisons between couples of different tunings. At each iteration, a human decision-maker expresses a preference between two calibrations (samples), highlighting which one, if any, is better than the other. The op...

This paper presents a model predictive controller for tracking periodic parametric reference curves. The controller is formulated in a single layer so that the timeparameterization of the reference curve, the trajectory planning, and the trajectory tracking tasks are solved in a single optimization problem which is computed at each sampling time. T...

Agriculture accounts for approximately 70% of the world’s freshwater consumption. Furthermore, traditional irrigation practices, which rely on empirical methods, result in excessive water usage. This, in turn, leads to increased working hours for irrigation pumps and higher electricity consumption. The main objective of this study is to develop and...

Wine is a relevant part of the diet in many countries, showing significant nutritional properties, providing health benefits to consumers, and having a significant weight in economy. Also, wine plays an important role in many cultures as a part of their social relationships, feasts, or religion where some of them may become a sign of luxury and dis...

Mathematical models are critical to understand the spread of pathogens in a population and evaluate the effectiveness of non-pharmaceutical interventions (NPIs). 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 them optimize a simple co...

Closed-loop glycemic control algorithms have demonstrated the ability to improve glucose regulation in patients with type 1 diabetes mellitus (T1D), both in silico and clinical trials. Many of the proposed control strategies have been developed, based on time-invariant linear models, without considering the parametric variations of T1DM subjects. I...

Although modeling studies are focused on the control of SIR-based systems describing epidemic data sets, few of them present a formal dynamic characterization in terms of the two main indexes: the infected peak prevalence (IPP) and the final epidemic size (EFS). These indices are directly related to equilibrium sets and stability, which are crucial...

Electric microgrids have become an interesting tool to facilitate the inclusion of renewable energies. Its architecture and control system plays a fundamental role in the implementation of these systems. This paper proposes a control strategy for the management of energy resources in a residential microgrid. The system is made up of a set of solar...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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....

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...