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

It has been proven that advanced building control, like model predictive control (MPC), can notably reduce the energy use and mitigate greenhouse gas emissions. However, despite intensive research efforts, the practical applications are still in the early stages. There is a growing need for multidisciplinary education on advanced control methods in the built environment to be accessible for a broad

Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big

In this paper, we address a scheduling problem belonging to a two-stage assembly system that can also be viewed as a mass customization system. The first stage of this system consists of a set of subassembly machines, each of which produces a component type. These components are then transferred in sublots to Stage 2, where they are assembled into finished products. Stage 2 consists of multiple parallel

This paper formulates a Model Predictive Control (MPC) policy to mitigate the COVID-19 contagion in Brazil, designed as optimal On-Off social isolation strategy. The proposed optimization algorithm is able to determine the time and duration of social distancing policies in the country. The achieved results are based on data from the period between March and May of 2020, regarding the cumulative number

Synchronization pertains to the property of interconnected systems according to which their dynamic behavior is coordinated in an appropriate sense. That is to say, some of their state variables, or functions of the latter for that matter, converge to each other. Synchronization may occur naturally or may be induced, controlled, and it may be present between two systems or among a large number. In

Anti-jerk controllers, commonly implemented in production vehicles, reduce the longitudinal acceleration oscillations transmitted to the passengers, which are caused by the torsional dynamics of the drivetrain during torque transients. Hence, these controllers enhance comfort, drivability, and drivetrain component durability. Although anti-jerk controllers are commonly implemented in conventional production

A key assumption in the development of system identification and adaptive control schemes is the availability of a regression model which is linear in the unknown parameters (of the plant and/or the controller). Applying standard— e.g., gradient descent-based—parameter estimators leads to a linear time-varying equation for the parameter errors, whose stability relies on the usually stringent persistency

Inverse optimal control (IOC) is a powerful theory that addresses the inverse problems in control systems, robotics, Machine Learning (ML) and optimization taking into account the optimal manners. This paper reviews the history of the IOC and Inverse Reinforcement Learning (IRL) approaches and describes the connections and differences between them to cover the research gap in the existing literature

This paper presents a tutorial of the role that mean-field-type games play in the design of risk-aware controllers. The tutorial is presented in an easy-to-follow manner for beginners, engineers and early career scientists. It starts by introducing a basic stochastic mean-field-free control design problem and progressively adds more complexity to the problem setup navigating through the risk-aware

Youla-Kucera (YK) parametrization was formulated decades ago for obtaining the set of controllers stabilizing a linear plant. This fundamental result of control theory has been used to develop theoretical tools solving many control problems ranging from stable controller switching, closed-loop identification, robust control, disturbance rejection, adaptive control to fault tolerant control. This paper

The performance of manufacturing networks relies on interactions. Digitalization supports an evidence-based understanding of partners’ idiosyncrasies and behaviours, so that proper decision-making takes place. Indeed, context uncertainty and flexible problem solving demand human involvement in cyber-physical systems. Therefore, to materialize systems efficiency along with flexibility and robustness

Around 30% to 70% of products in retail and services experience low demand, including spare parts and components for nearly all types of machinery and equipment industries. A detailed analysis of stock forecasting methods for the low demand represents a research gap in inventory management. The existing clustering methods, that is, ABC analysis and XYZ analysis (based on coefficient of variation),

Through the introduction of stochastic operators, the following work is an extension of previous works on robustness and resilience of compartmentalized closed-loop input-output systems. Indeed, those can be subject to the onset of external random perturbations which compromise the functioning of the input-output processing. The stochastic version of the network Price identity shows that the amplified

A manufacturing system able to perform a high variety of tasks requires different types of resources. Fully automated systems using robots possess high speed, accuracy, tirelessness, and force, but they are expensive. On the other hand, human workers are intelligent, creative, flexible, and able to work with different tools in different situations. A combination of these resources forms a human-machine/robot

Despite the enormous benefit that has accrued to society from control technology and the continued vitality of control science as a research field, there is broad consensus that the practitioners of control and the academic research community are insufficiently engaged with each other. We explore this concern with reference to the oft-cited theory/practice gap but also from an industry perspective

The paper presents an improved formula for optimal navigation with respect to time which can be applied to the practical methods and numerical techniques for computing minimum-time paths and routing in arbitary winds, i.e. strong, critical, weak including their combinations, considered on arbitrary modelling surface, thought of as a Riemannian manifold of dimension two. It admits space and time dependence

In the context of the mainstream of globalisation and a new trend of slowbalisation, we review the existing literature including both empirical and analytical papers on the sourcing and location decisions in Supply Chains. After defining the different sourcing strategies, e.g., insourcing, outsourcing, offshoring and reshoring, we present the drivers for each strategy and how they can be incorporated

Due to high computational load of ideal realization of fractional order elements, fractional order transfer functions are commonly implemented via integer-order, limited-band approximate models. An important side effect of such a non-ideal fractional order controller function realization for control applications is that the approximate fractional order models may deteriorate practical performance of

A robust maximum power point tracking (MPPT) control is of paramount importance in the performance enhancement and the optimization of photovoltaic systems (PVSs). Solar panel exhibits nonlinear behavior under real climatic conditions and output power fluctuates with the variation in solar irradiance and temperature. Therefore, a control strategy is requisite to extract maximum power from solar panels

The quest for autonomy has been a pervasive theme in human culture through-out history. In this paper a general definition of autonomous systems is presented and discussed that leads naturally to the establishment of metrics to measure the level of autonomy of a system. This definition is based on the systems ability to achieve goals under uncertainties and it does not involve the means by which the

Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified,

The way humans work in production and logistics systems is changing. The evolution of technologies, Industry 4.0 applications, and societal changes, such as ageing workforces, are transforming operations processes. This transformation is still a “black-box” for many companies, and there are calls for new management approaches that can help to successfully overcome the future challenges in production

Many multi-agent control algorithms and dynamic agent-based models arising in natural and social sciences are based on the principle of iterative averaging. Each agent is associated to a value of interest, which may represent, for instance, the opinion of an individual in a social group, the velocity vector of a mobile robot in a flock, or the measurement of a sensor within a sensor network. This value

We overview a recent research activity where suitable reset actions induce stability and performance of PID-controlled positioning systems suffering from nonlinear frictional effects. With a Coulomb-only effect, PID feedback produces a set of equilibria whose asymptotic (but not exponential) stability can be certified by using a discontinuous Lyapunov-like function. With velocity weakening effects

Aerial manipulators are composed of a robotic arm installed on an unmanned aerial vehicle and are used in several applications because of their inherent ability in performing complex tasks. In real-world applications, these systems are required to be robust against exogenous disturbances, such as wind, to guarantee the desired level of accuracy in the execution of the tasks. In this paper, the reference

This paper presents new perspectives on the application of Artificial Intelligence (AI) solutions to process Spacecraft (S/C) flight data in order to augment currently used operational S/C health monitoring and diagnostics systems. It captures the growing general interest in the usage of such techniques in the Space engineering domain and applications. Jointly with the AI approach, the operational

This paper will review some fundamental results in the understanding of several basic problems concerning feedback and uncertainty. First, we will consider adaptive control of linear stochastic systems, in particular, the global stability and optimality of the well-known self-tuning regulators, designed by combining the least-squares estimator with the minimum variance controller. This natural and

Robotic manipulation aims at combining the versatility and flexibility of mobile robot platforms with manipulation capabilities of robot manipulators. This survey paper comprehensively reviews the state-of-the-art development of collaborative robotic manipulation from the perspective of modelling, control and optimization. Then, the recent results in this field can be categorized into coordination

In this position paper, we summarize history, current activities and future topics of IFAC Technical Committee (TC) 5.2 “Manufacturing Modelling for Management and Control”. As a special focus, we discuss the results of the 9th IFAC Conference MIM 2019 that was recently organized by IFAC TC 5.2 in Berlin, Germany and attended by 740 participants. We analyse the current activities of the working groups

To achieve the safe, reliable autonomous operation of spacecraft, research on the fault diagnosis of control systems has attracted the attention of engineers and academicians throughout the aerospace field. Diagnosability can characterize the fault diagnosis capability of control systems. Connecting diagnosability analysis to the design of a spacecraft control system’s structure and diagnosis method

The dynamic nature of sustainable energy and electric systems can vary significantly along with the environment and load change, and they represent the features of multivariate, high complexity and uncertainty of the nonlinear system. Moreover, the integration of intermittent renewable energy sources and energy consumption behaviours of households introduce more uncertainty into sustainable energy

Recently, multiple unmanned vehicles have attracted a great deal of attention as viable solutions to a wide variety of civilian and military applications. Among many topics in the field of multiple unmanned systems, formation control and coordination is of great importance. This paper presents a comprehensive literature review on the strategies and methodologies applied for formation control of multiple

This paper focuses on the algebraic expression of discrete event systems (DES), which bridges the gap between linear control system theory and discrete event system theory. For this purpose, we firstly give an overview of different approaches towards an algebraic description existing in the literature in the area of different classes of DES, such as Petri nets, automata and Boolean networks. After