Fabrizio Dabbene

Italian National Research Council | CNR · Institute of Electronics, Computer and Telecommunication Engineering IEIIT

This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete...

Mobility will surely be at the core of the Smart Cities of the future. As such, it must be planned based on novel mobility models, smart enough to answer the multifaceted needs of users, while being sustainable and energy efficient. In this evolution, Electric Vehicles (EVs) will be crucial, as confirmed by the fact that many governments are alread...

The advancement of robotics and autonomous systems will be central to the transition of space missions from current geocentric architectures to self-sustainable, independent systems. Every future mission architecture will heavily rely on the ability to autonomously rendezvous and mating multiple elements in space. For these critical capabilities to...

In this article, we consider a dynamic model for competition in a social network, where two strategic agents have fixed beliefs, and the nonstrategic/regular agents adjust their states according to a distributed consensus protocol. We suppose that one strategic agent must identify $k_+$ target agents in the network to maximally spread his/her own...

In precision agriculture, remote sensing is an essential phase in assessing crop status and variability when considering both the spatial and the temporal dimensions. To this aim, the use of unmanned aerial vehicles (UAVs) is growing in popularity, allowing for the autonomous performance of a variety of in-field tasks which are not limited to scout...

In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of the approximating set, by defining families of simple-approximating sets of given complexity. A probabilistic scaling procedure then scales these sets to obtain the desi...

In the last years, unmanned aerial vehicles are becoming a reality in the context of precision agriculture, mainly for monitoring, patrolling and remote sensing tasks, but also for 3D map reconstruction. In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an ap...

It has been long known that malicious content, e.g., fake news, originates from bots operating on fringe social networks (e.g., the now-defunct Parler) and then percolate to mainstream social networks (e.g., Twitter). It follows that effective moderation in mainstream networks depends upon proactively identifying malicious content while it...

Stochastic Model Predictive Control (MPC) gained popularity thanks to its capability of overcoming the conservativeness of robust approaches, at the expense of a higher computational demand. This represents a critical issue especially for sampling-based methods. In this letter we propose a policy learning MPC approach, which aims at reducing the co...

In modern society, individuals' opinions on various topics evolve as the result of their continuous interactions and are shaped by interpersonal influences and individual social power. Friedkin's reflected appraisal theory reveals how social power evolves along discussion sequences as a consequence of direct and indirect interpersonal influence ove...

Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approa...

Agriculture 4.0 comprises a set of technologies that combines sensors, information systems , enhanced machinery, and informed management with the objective of optimising production by accounting for variabilities and uncertainties within agricultural systems. Autonomous ground and aerial vehicles can lead to favourable improvements in management by...

In this paper, we address environmental monitoring for 3-D map reconstruction using drone networks. In view of the fact that the 3-D reconstruction requires images from a variety of viewing angles, we first formulate a novel angle-aware coverage control problem, based on a concept of virtual field that combines the position of a monitoring target a...

To properly locate and operate autonomous vehicles for in-field tasks, the knowledge of their instantaneous position needs to be combined with an accurate spatial description of their environment. In agricultural fields, when operating inside the crops, GPS data are not reliable nor always available, therefore high-precision maps are difficult to b...

Towards a unified system theory of opinion formation and social influence Processes of information diffusion over social networks (for example, opinions spread and 2 beliefs formation) are attracting substantial interest to various disciplines ranging from behavioral sciences to mathematics and engineering. Since the opinions and behaviors of each...

It is known from the literature that solutions of homogeneous linear stable difference equations may experience large deviations, or peaks, from the nonzero initial conditions at finite time instants. While the problem has been studied from a deterministic standpoint, not much is known about the probability of occurrence of such event when both the...

In this paper, we address the probabilistic error quantification of a general class of prediction methods. We consider a given prediction model and show how to obtain, through a sample-based approach, a probabilistic upper bound on the absolute value of the prediction error. The proposed scheme is based on a probabilistic scaling methodology in whi...

In this paper, we address the probabilistic error quantification of a general class of prediction methods. We consider a given prediction model and show how to obtain, through a sample-based approach, a probabilistic upper bound on the absolute value of the prediction error. The proposed scheme is based on a probabilistic scaling methodology in whi...

We consider a dynamic model for competition in a social network, where two strategic agents have fixed beliefs and the non-strategic/regular agents adjust their states according to a distributed consensus protocol. We suppose that one strategic agent must identify k+ target agents in the network in order to maximally spread its own opinion and alte...

In this article, we address the problem of inferring direct influences in social networks from partial samples of a class of opinion dynamics. The interest is motivated by the study of several complex systems arising in social sciences, where a population of agents interacts according to a communication graph. These dynamics over networks often exh...

Designing a static state-feedback controller subject to structural constraint achieving asymptotic stability is a relevant problem with many applications, including network decentralized control, coordinated control, and sparse feedback design. Leveraging on the Projection Lemma, this work presents a new solution to a class of state-feedback contro...

In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of the approximating set, by defining families of simple-approximating sets of given complexity. A probabilistic scaling procedure then allows to rescale these sets to obta...

Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Autonomous ground and aerial vehicle can lead to favorable improvements in management by performing in-field...

Inspired by the increasing attention of the scientific community towards the understanding of human relationships and actions in social sciences, in this paper we address the problem of inferring from voting data the hidden influence on individuals from competing ideology groups. As a case study, we present an analysis of the closeness of members o...

Photogrammetry Big data UAV remote sensing Semantic interpretation 3D point cloud segmentation In precision agriculture, autonomous ground and aerial vehicles can lead to favourable improvements in field operations, extending crop scouting to large fields and performing field tasks in a timely and effective way. However, automated navigation and op...

Interpersonal influence estimation from empirical data is a central challenge in the study of social structures and dynamics. Opinion dynamics theory is a young interdisciplinary science that studies opinion formation in social networks and has a huge potential in applications, such as marketing, advertisement and recommendations. The term social i...

Dynamics and control of processes over social networks, such as the evolution of opinions, social influence and interpersonal appraisals, diffusion of information and misinformation, emergence and dissociation of communities, are now attracting significant attention from the broad research community that works on systems, control, identification an...

In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, which has restricted their applicability to slow-dynamics and high-performing systems. To reduce the computational burden, in this paper we extend the probabilistic scaling a...

Distributed multi-agent optimization is a key methodology to solve problems arising in large-scale networks, including recent energy management systems that consist of such many entities as suppliers, consumers, and aggregators, who behave as independent agents. In this chapter, we focus on distributed multi-agent optimization based on the linear c...

The article introduces novel methodologies for the identification of coefficients of switching autoregressive moving average with exogenous input systems and switched autoregressive exogenous linear models. We consider cases where system's outputs are contaminated by possibly large values of noise for both cases of measurement noise and process noi...

In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive assumption on either independence nor Gaussianity. We revisit the rather classical approach based on penalty functions, with the aim of designing a control scheme that m...

Mobility is undergoing dramatic transformations. Especially in the context of urban areas, several significant changes are underway, driven by both new mobility needs and environmental concerns. The most mature one, which still is struggling to affirm itself is the process of the adoption of Electric Vehicles (EVs), thus switching from fuel-based t...

In this paper we consider the problem of designing optimal H∞ static state feedback control in the presence of structural constraints on the feedback gain. This problem arises in many applications, such as Network Decentralized Control and Overlapping Control, where the controller is constrained to have a specific nonzero patterns. Building upon pr...

From the literature, it is known that solutions of homogenous linear stable difference equations may experience large deviations, or peaks, from the nonzero initial conditions at finite time instants. In this paper we take a probabilistic standpoint to analyze these phenomena by assuming that both the initial conditions and the coefficients of the...

In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive assumption on either independence nor Gaussianity. We revisit the rather classical approach based on penalty functions, with the aim of designing a control scheme that m...

Crop monitoring and farm activities with innovative systems, as Unmanned Aerial Vehicles, is an undergoing research known as fourth agricultural revolution. In our paper, two different farming scenarios are proposed, in which a trajectory tracking based on Model Predictive Control is proposed in combination with a waypoint-based guidance algorithm....

In this paper a novel approach to co-design controller and attack detector for nonlinear cyber-physical systems affected by false data injection (FDI) attack is proposed. We augment the model predictive controller with an additional constraint requiring the future---in some steps ahead---trajectory of the system to remain in some time-invariant nei...

The paper introduces novel methodologies for the identification of coefficients of switched autoregressive and switched autoregressive exogenous linear models. We consider cases which system's outputs are contaminated by possibly large values of noise for the both case of measurement noise in switched autoregressive models and process noise in swit...

Designing a static state-feedback controller subject to structural constraint achieving asymptotic stability is a relevant problem with many applications, including network decentralized control, coordinated control, and sparse feedback design. Leveraging on the Projection Lemma, this work presents a new solution to a class of state-feedback contro...

The increasing penetration of renewable energy resources, paired with the fact that load can vary significantly, introduce a high degree of uncertainty in the behavior of modern power grids. Given that classical dispatch solutions are “rigid,” their performance in such an uncertain environment is in general far from optimal. For this reason, in thi...

Static output feedback design for linear plants is well known to be a challenging non-convex problem. The presence of plant uncertainty makes this challenge even harder. In this chapter, we propose a new BMI formulation with S-variables which includes an interesting link between state feedback, output injection , state injection and static output f...

In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need of real-time implementability of guidance and control strategies for a...

Motivated by the increasing interest of the control community towards social sciences and the study of opinion formation and belief systems, in this paper we address the problem of exploiting voting data for inferring the underlying affinity of individuals to competing ideology groups. In particular, we mine key voting records of the Italian Senate...

This chapter presents recent solutions to the optimal power flow (OPF) problem in the presence of renewable energy sources (RES), {such} as solar photo-voltaic and wind generation. After introducing the original formulation of the problem, arising from the combination of economic dispatch and power flow, we provide a brief overview of the different...

In this paper, we provide an overview of recent works on dynamics of social networks and distributed algorithms for their exploration, contributed by Dr. Roberto Tempo (1956–2017) and his colleagues. These works, based on the recent achievements in multi-agent systems theory and distributed randomized algorithms, contribute in bridging the gap betw...

In this paper, a guidance and tracking control strategy for fixed-wing unmanned aerial vehicle autopilots is presented. The proposed control exploits recent results on samplebased stochastic model predictive control, which allows coping in a computationally efficient way with both parametric uncertainty and additive random noise. Different applicat...

In this paper, a guidance and tracking control strategy for fixed-wing Unmanned Aerial Vehicle (UAV) autopilots is presented. The proposed control exploits recent results on sample-based stochastic Model Predictive Control, which allow coping in a computationally efficient way with both parametric uncertainty and additive random noise. Different ap...

In this paper, we propose a technique for the estimation of the influence matrix in a sparse social network, in which $n$ individual communicate in a gossip way. At each step, a random subset of the social actors is active and interacts with randomly chosen neighbors. The opinions evolve according to a Friedkin and Johnsen mechanism, in which the i...

The paper introduces a novel methodology for the identification of the coefficients of switched autoregressive exogenous (SARX) linear models. We consider the case when the system's outputs are contaminated by possibly large values of measurement noise. Partial information on the noise is available, in the form of the knowledge of a finite number o...

Recounts the life and professional achievements of Roberto Tempo. Tempo was not only a first-class scholar but also a strong believer in the importance of serving the control community. In his illustrious career, he was heavily involved in the IEEE Control Systems Society (CSS) and IFAC activities. His service in many different editorial roles was...

Rendezvous orbital maneuvers are planned operations which intend to make two spacecraft meet, while avoiding collisions. Aspects such as trajectory safety and robustness, as well as obstacle avoidance, are fundamental for the mission success. The aim of this paper is to provide an overview of some recent algorithms for guidance and control systems,...

Static output feedback design for linear plants is well known to be a challenging non-convex problem. The presence of plant uncertainty makes this challenge even harder. In this chapter, we propose a new BMI formulation with S-variables which includes an interesting link between state feedback, output injection, state injection, and static output f...

In the last decade, different indoor flight navigation systems for small Unmanned Aerial Vehicles (UAVs) have been investigated, with a special focus on different configurations and on sensor technologies. The main idea of this paper is to propose a distributed Guidance Navigation and Control (GNC) system architecture, based on Robotic Operation Sy...

This paper considers the problem of designing a control strategy for proximity operations of space systems. The considered setup is realistic, and is based on a model derived and validated on an experimental test-bed. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space...

The aim of this paper is to propose a novel methodology for estimating the social influence among agents interacting in a sparse social network described by the Friedkin and Johnsen model. In this classical model, n agents discuss m ≪ n topics, which are influenced by the others' opinions, but are not completely open minded, being persistently driv...

In this paper, we address the problem of identification of Markovian jump ARX systems in the case where there is no a priori knowledge on the statistics of the process noise. The only assumptions made are the availability of known upper bounds on the subsystems order and noise level. The proposed method leverages available results to identify the s...

We present a novel solution algorithm for a specific set of linear equations arising in large scale sparse interconnections, such as the PageRank problem. The algorithm is distributed, exploiting the underlying graph structure, and completely asynchronous. The main feature of the proposed algorithm is that it ensures that the consistency constraint...

The IEEE Control Systems Society (CSS) Publication Activities report comprises reports provided by the editors-in-chief of the Society publications, namely, IEEE Transactions on Automatic Control (TAC), IEEE Transactions on Control Systems Technology (TCST), IEEE Transactions on Control of Network Systems (TCNS), and IEEE Control Systems Magazine (...

The increasing penetration of renewable energy resources, together with the fact that load can vary significantly, introduce a high degree of uncertainty in the behavior of modern power grids. Given that classical dispatch solutions are "rigid," their performance in such an uncertain environment is in general far from optimal. For this reason, in t...

In this paper, the problem of identifying nonlinear systems under adaptive binary-valued output measurements is considered. We follow a nonparametric approach, which directly estimates the value of the nonlinear function representing the system at any fixed point with the help of a recursive kernel-based stochastic approximation algorithm with expa...

Challenges and opportunities of Radio-Frequency Identification (RFID) adoption for food traceability are treated and evaluated considering, in food supply chains, the different areas (production process, logistics, and distribution) and applications (process, warehouse and retail management, cold chain monitoring, logistic, intelligent packaging, a...

R-RoMulOC is a freely distributed toolbox which aims at making easily available to the users different optimization-based methods for dealing with uncertain systems. It implements both deterministic LMI-based results, that provide guaranteed performances for all values of the uncertainties, and probabilistic randomization-based approaches, that gua...

In this paper, probabilistic guarantees for constraint sampling of multistage robust convex optimization problems are derived. The dynamic nature of these problems is tackled via the so-called scenario-with-certificates approach. This allows to avoid the conservative use of explicit parametrizations through decision rules, and provides a significan...