In this paper, we investigate the problem of designing event-triggered controllers for containing epidemic processes in complex networks. We focus on a deterministic susceptible–infected–susceptible (SIS) model, which is one of the well-known, fundamental models that capture the epidemic spreading. The event-triggered control is particularly formulated in the context of viral spreading, in which control

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged

A lot of automatic feedback control and learning tasks carried out on many dynamical systems still fundamentally rely on a form of proportional–integral–derivative (PID) control law. The PID law is often viewed as a simplistic computational control algorithm. However just like all non-convex optimization problems, tuning the PID algorithm for accurate and stable closed-loop control becomes a NP-Hard

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different

Many systems in the natural and physical world often work in unison with similar other systems. This process of simultaneous operation is known as synchronization. In the past few decades, owing to this phenomenon’s importance, extensive research efforts have been made. However, many of the existing results consider the systems are identical and/or linear time-invariant, while practical systems are

In this paper we envision how to tackle a particularly challenging problem which presents highly interdisciplinary features, ranging from biology to engineering: the dynamic description and technological realisation of flapping flight. This document explains why, in order to gain new insights into this topic, we chose to employ port-Hamiltonian theory. We discuss how the physically unifying character

The decentralized aggregate motion of many individual robots is known as robotic flocking. The study of robotic flocking has received considerable attention in the past twenty years. As we begin to deploy flocking control algorithms on physical multi-agent and swarm systems, there is an increasing necessity for rigorous promises on safety and performance. In this paper, we present an overview the literature

Researchers in biomechanics, neuroscience, human–machine interaction and other fields are interested in inferring human intentions and objectives from observed actions. The problem of inferring objectives from observations has received extensive theoretical and methodological development from both the controls and machine learning communities. In this paper, we provide an integrating view of objective

Prognostic and systems Health Management (PHM) is an integral part of a system. It is used for solving reliability problems that often manifest due to complexities in design, manufacturing, operating environment and system maintenance. For safety-critical applications, using a model-based development process for complex systems might not always be ideal but it is equally important to establish the

Integrated Chassis Control (ICC) is one of the most appealing subjects for vehicle dynamics specialists and researchers, due to the increasing number of chassis actuators of modern human-driven and automated cars. ICC ensures that the potential of the available actuators is systematically exploited, by overcoming the individual limitations, and solving conflicts and redundancies, which results into

Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution

Simultaneous Localisation and Mapping (SLAM) is the archetypal chicken and egg problem: Localisation of a robot with respect to a map requires an estimate of the map, while mapping an environment from data acquired by a robot requires an estimate of the robot localisation. The nonlinearity and co-dependence of the SLAM problem has made it an ongoing research problem for more than thirty years. The

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional

Nowadays, one important challenge in cyber-physical production systems is updating dynamic production schedules through an automated decision-making performed while the production is running. The condition of the manufacturing equipment may in fact lead to schedule unfeasibility or inefficiency, thus requiring responsiveness to preserve productivity and reduce the operational costs. In order to address

Digital transformation can provide a competitive edge for many manufacturers, however many smaller companies may not have the capabilities needed to embrace this opportunity and may be left behind. This paper reports on an approach which is attempting to alleviate this by creating a low-cost pathway to help manufacturing small and medium sized enterprises (SMEs) engage with digitalisation. This paper

Manufacturing systems are frequently required to adapt to changes. To support these, a standardized, semantic exchange of information between all components of the system is crucial. An information model describing all information available within the manufacturing system together with a common middleware protocol is able to fulfil this requirement. To create an information model, it is relevant to

Oriented to the new generation of cyber-physical manufacturing, this paper introduces two breakthroughs of industrial design and development software system: model-based systems engineering (MBSE) and cloud computing. Based on the classic V model for product development, an MBSE double-Vs model is refined which takes both model life-cycle and product life-cycle into consideration for design process

Social distancing as a form of nonpharmaceutical intervention has been enacted in many countries as a form of mitigating the spread of COVID-19. There has been a large interest in mathematical modeling to aid in the prediction of both the total infected population and virus-related deaths, as well as to aid government agencies in decision making. As the virus continues to spread, there are both economic

Quadcopters are increasingly used for applications ranging from hobby to industrial products and services. This paper serves as a tutorial on the design, simulation, implementation, and experimental outdoor testing of digital quadcopter flight controllers, including Explicit Model Predictive Control, Linear Quadratic Regulator, and Proportional Integral Derivative. A quadcopter was flown in an outdoor

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 treatment will be able to clear the infection. In addition

We propose a multitask learning approach to learn the parameters of a compartmental discrete-time epidemic model from various data sources and use it to design optimal control strategies of human-mobility restrictions that both curb the epidemic and minimize the economic costs associated with implementing non-pharmaceutical interventions. We develop an extension of the SEIR epidemic model that captures

Crowds are a source of transmission in the COVID-19 spread. Contention and mitigation measures have focused on reducing people’s mass gathering. Such efforts have led to a drop in the economy. The application of a vaccine at a world level represents a grand challenge for humanity, and it is not likely to accomplish even within months. In the meantime, we still need tools to allow the people integration

Fault prognosis of discrete event systems (DES) is an active field of research and has become important due to the high demand on systems’ safety and reliability. The aim of this paper is to provide the state-of-the-art on fault prognosis of DES, as well as quick directions regarding many aspects of the research on this topic. This overview was carried out using a systematic approach, which allowed

We address the prediction of the number of new cases and deaths for the coronavirus disease 2019 (COVID-19) over a future horizon from historical data (forecasting). We use a model-based approach based on a stochastic Susceptible–Infections–Removed (SIR) model with time-varying parameters, which captures the evolution of the disease dynamics in response to changes in social behavior, non-pharmaceutical

In this paper, we revisit the problem of robust diagnosability of Discrete-Event Systems (DES), and present a comparative analysis between the following notions of robust diagnosability existing in the literature: (i) diagnosability of DES subject to permanent sensor failures, assuming that sensors may fail only before the first occurrence of the events they are supposed to record; (ii) diagnosability

New technologies supporting cyber-physical enterprise systems with respect to online decision-making based on up-to-date data, require networked sensor and actor systems in place. Interoperability is a key factor when supporting systems in a system-of-systems. In this paper, we survey approaches on Enterprise Interoperability with special attention to the Cyber-Physical Manufacturing Enterprise. The

This paper is based on the observation that, during Covid-19 epidemic, the choice of which individuals should be tested has an important impact on the effectiveness of selective confinement measures. This decision problem is closely related to the problem of optimal sensor selection, which is a very active research subject in control engineering. The goal of this paper is to propose a policy to smartly

With the development of remote sensing techniques, the fusion of multimodal data, particularly hyperspectral-Light Detection And Ranging (HS-LiDAR) and hyperspectral-SAR, has become an important research field in numerous application areas. Multispectral, HS, LiDAR, and Synthetic Aperture Radar (SAR) images contain detailed information about the monitored surface that are complementary to each other

Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying

This paper shows how existing methods of time series analysis and modeling can be exploited in novel ways to monitor and forecast the COVID-19 epidemic. In the past, epidemics have been monitored by various statistical and model metrics, such as evaluation of the effective reproduction number, R(t). However, R(t) can be difficult and time consuming to compute. This paper suggests two relatively simple

In this paper, a new version of the well-known epidemic mathematical SEIR model is used to analyze the pandemic course of COVID-19 in eight different countries. One of the proposed model’s improvements is to reflect the societal feedback on the disease and confinement features. The SEIR model parameters are allowed to be time-varying, and the ranges of their values are identified by using publicly

In this paper, we present an overview of several challenges in arable farming that are well suited for research by the control engineering society. We discuss the global needs that these challenges are related to as well as the relation of these challenges to future applications of arable farming. For each of these opportunities we provide several concrete and detailed research questions. Particular

This paper presents a data-based simple model for fitting the available data of the Covid-19 pandemic evolution in France. The time series concerning the 13 regions of mainland France have been considered for fitting and validating the model. An extremely simple, two-dimensional model with only two parameters demonstrated to be able to reproduce the time series concerning the number of daily demises

We investigate adaptive strategies to robustly and optimally control the COVID-19 pandemic via social distancing measures based on the example of Germany. Our goal is to minimize the number of fatalities over the course of two years without inducing excessive social costs. We consider a tailored model of the German COVID-19 outbreak with different parameter sets to design and validate our approach

The diffusion of COVID-19 represents a real threat for the health and economic system of a country. Therefore the governments have to adopt fast containment measures in order to stop its spread and to prevent the related devastating consequences. In this paper, a technique is proposed to optimally design the lock-down and reopening policies so as to minimize an aggregate cost function accounting for

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well

An adaptive-learning model predictive control (AL-MPC) framework is proposed for incorporating disturbance prediction, model uncertainty quantification, pattern learning, and recursive subspace identification for use in controlling complex dynamic systems with periodically recurring large random disturbances. The AL-MPC integrates online learning from historical data to predict the future evolution

This paper is concerned with data science and analytics as applied to data from dynamic systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in industrial operation data. Therefore, we focus on latent variable methods that achieve dimension reduction and collinearity removal. We present a new dimension reduction expression of state space framework to unify dynamic

This paper discusses the theory of oscillatory normal modes, and its extension to general multi-body mechanical systems. We first review the efforts to generalize modal analysis to the non-linear case. This body of knowledge is vast and spread across several subfields of mathematics, physics, and engineering. We concisely summarize these results, and connect them together using a language familiar

Modern modeling languages for general physical systems, such as Modelica, Amesim, or Simscape, rely on Differential Algebraic Equations (DAEs), i.e., constraints of the form f(x′,x,u)=0. This drastically facilitates modeling from first principles of the physics, as well as the reuse of models. In this paper, we develop the mathematical theory needed to establish the development of compilers and tools

For a system, a priori identifiability is a theoretical property depending only on the model and guarantees that its parameters can be uniquely determined from observations. This paper provides a survey of the various and numerous definitions of a priori identifiability given in the literature, for both deterministic continuous and discrete-time models. A classification is done by distinguishing analytical

The analysis of equipment degradation has traditionally developed in two main directions. One approach, of great interest for control system design, has been to consider that degradation causes fundamental changes to the behaviour of a system. Another approach, used in optimal maintenance planning and production scheduling, considers degradation as a separate process that affects performance but does

The widespread popularity of unmanned aerial vehicles enables an immense amount of power line inspection data to be collected. It is an urgent issue to employ massive data especially the visible images to maintain the reliability, safety, and sustainability of power transmission. To date, substantial works have been conducted on the data analysis for power line inspection. With the aim of providing

We present and discuss a variety of mathematical models that have been proposed to capture the dynamic behavior of epidemic processes. We first present traditional group models for which no underlying graph structures are assumed, thus implying that instantaneous mixing between all members of a population occurs. Then we consider models driven by similar principles, but involving non-trivial networks

The purpose of this work is to give a contribution to the understanding of the COVID-19 contagion in Italy. To this end, we developed a modified Susceptible-Infected-Recovered-Deceased (SIRD) model for the contagion, and we used official data of the pandemic for identifying the parameters of this model. Our approach features two main non-standard aspects. The first one is that model parameters can

The SARS-CoV-2 is a type of coronavirus that has caused the pandemic known as the Coronavirus Disease of 2019, or COVID-19. In traditional epidemiological models such as SEIR (Susceptible, Exposed, Infected, Removed), the exposed group E does not infect the susceptible group S. A distinguishing feature of COVID-19 is that, unlike with previous viral diseases, there is a distinct “asymptomatic” group

The concept of digital ecosystem (DES) is widely used in autonomous manufacturing process control and the development of complex, stable, interactive, self-organizing and reliable management systems for various industries. The paper offers a concept of DES control system architecture based on predictive models. For estimating and predicting the state of resources in production processes, an approach

The next-generation systems are expected to be largely cyber–physical systems (CPSs) that autonomously control physical processes, through sensors and actuators typically in real-time feedback and cooperative control loops distributed among physical and cyber environments. The rapid technological advancements enhance the smartness of these CPSs, pushing their boundaries of performance and efficiency

This paper presents a review of past and present results and approaches in the area of motion planning using MIP (Mixed-integer Programming). Although in the early 2000s MIP was still seen with reluctance as method for solving motion planning-related problems, nowadays, due to increases in computational power and theoretical advances, its extensive modeling capabilities and versatility are coming to

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. A complete analysis of the main dynamic characteristic

An extension of the classical pandemic SIRD model is considered for the regional spread of COVID-19 in France under lockdown strategies. This compartment model divides the infected and the recovered individuals into undetected and detected compartments respectively. By fitting the extended model to the real detected data during the lockdown, an optimization algorithm is used to derive the optimal parameters

Two mathematical models of the COVID-19 dynamics are considered as the health system in some country consists in a network of regional hospital centers. The first macroscopic model for the virus dynamics at the level of the general population of the country is derived from a standard SIR model. The second local model refers to a single node of the health system network, i.e. it models the flows of

The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target