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In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the leader communicates its one-step future position to the drone, which keeps the formation along the desired trajectory. Inequality constraints are introduced in a switching control fashion to the leader’s NMPC formulation to avoid obstacles. In the literature, few works using NMPC and NMHE deal with these two vehicles together. Moreover, the presented scheme can tackle noisy, partial, and missing measurements of the agents’ state. Results show that the ground car can avoid detected obstacles, keeping the tracking errors of both robots in the order of a few centimeters, thanks to trustworthy NMHE estimates and NMPC predictions.

The transport of Dangerous Goods (DG) in Italy occurs mainly via road networks as part of supply chain logistics. Although they account for a relatively small proportion of overall freight, accidents involving DG can have significant consequences and are classified as Low Probability High Consequence (LPHC) events. These events, although rare, present considerable risks for both the environment and the public, particularly in the face of the increase in industrial disasters. This paper presents a data-driven analysis aimed at quantifying the rates and frequencies of accidents associated with the transport of DG in the Genoa district. Using a deterministic risk approach, the study identifies the least DG transport routes and assesses risk levels accordingly. The integration of the Internet of Things (IoT) and Information and Communication Technologies (ICT) allows real-time monitoring of the transport of DG, enabling the identification of accident frequencies within defined spatial and temporal parameters. Using ICT tools and the database of National Fire and Rescue Service (NFRS), accidents involving trucks transporting DG are identified and analyzed. The study highlights the importance of rapid and effective emergency response processes to minimize individual damage and mitigate environmental and property damage. This research provides valuable information for risk assessment and emergency response planning in the Genoa district and highlights the importance of taking advantage of data-driven approaches to improve DG transport safety.

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems’ interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure.

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.

Tourism has become a dominant economic activity in coastal, historical, and mountainous locations. To address the negative impacts of mass tourist arrivals, this study presents an approach for promoting Sustainable Tourism Development in small, vulnerable destinations. Specifically, we focus on mitigating the adverse effects of short-stay tourism, which often leads to peak tourist densities and safety concerns. We propose a novel touristic trip design model, aiming to reduce peak tourist demand by optimizing scheduling in strategic destinations. This approach considers three critical aspects: maximizing tourist satisfaction, optimizing transport resource usage, and respecting maximum carrying capacities. Methodologically, we adopt a Network Flow Problem formulation solved using a Time Expanding Network. A Time Expanding Network provides a static representation of the network for each discrete time interval, ensuring travelers’ satisfaction and the overall duration of their visit. We conducted a case study in Liguria Region, Italy, comparing our model’s performance with an innovative heuristic approach. Results show the superiority of our approach in managing tourist flows and promoting sustainability. This research contributes valuable insights for achieving Sustainable Tourism Development in vulnerable touristic areas, assisting policymakers and stakeholders in making informed decisions for harmonizing tourism and environmental preservation.

This paper introduces a dynamic scheduling algorithm designed to minimize makespan within a smart manufacturing system, accommodating delays in the production process. The proposed approach relies on Model Predictive Control (MPC) principles and adapts flow-shop scheduling theory to solve an open-shop scheduling problem. It aims to strike a balance between the ideal, delay-free solution and robustness in the case of processing time delays. By combining MPC theory with flow-shop scheduling, the algorithm offers a robust approach to open-shop scheduling problems, even with uncertain processing times. Iterated upon the arrival of each new job on the shop floor, the algorithm incorporates a control horizon to predict impending job arrivals and seamlessly integrates them into the scheduling process. Efficiency is examined through a comprehensive case study, where it is compared against a similar, offline scheduling algorithm. This novel method not only optimizes scheduling but also adapts to dynamic scenarios, reducing the computational demand and the information needed to optimize the production process, thus making it suitable for agile manufacturing environments. The results demonstrate the algorithm’s efficacy in achieving competitive scheduling performance with nearly the same makespan as the offline algorithm, while accounting for uncertainties in processing times. A robustness analysis confirms the reliability of the proposed approach, showing an average improvement of 5% in makespan across different delay magnitudes.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

Hazardous materials (hazmat) transportation represents, since decades, one of the main issues in the accidental risk assessment and management on road network, above all, in urban areas. This kind of transport counts a small share in respect to the overall freight transport by road in EU-28, but it continues to arouse interest due to the catastrophic consequences that its involvement may concern in case of accidents. This paper presents the development of an ICT based system to detect constantly the hazmat vehicles on the roads and to map, in real time, the potential impact area generated by the specific transported product in case of road accident. The proposed system has been realized in the framework of the European Project “LOSE+”, (Logistic and Safety of freight transport) financed by the European Regional Development Fund (FESR) in the framework of the Interreg Italy-France Maritime Programme 2014-2020. The LOSE+ system consists of two main components: firstly, a network of cameras has been installed on the main arterial roads in the city of Genoa, regional capital of Liguria Region (Italy), along the coastal area near the port gates; secondly, a web-GIS platform, which in real time, provides the users with georeferenced maps related to the potential impact area of a possible accident, which involved the identified hazmat vehicles. Furthermore, the map images may be overlapped to other significant layers related to the land use and to sensible and vulnerable targets on the territory, such as hospitals or schools, modelled by the (Short-Cut Method, 2005) for the computation of the consequences analysis. The proposed LOSE+LAB system aims at providing to Public Authorities a smart tool to monitor and control hazmat circulation.

The definition of train scheduling is going to shift its current static approach, as generally resulting in a timetable, towards a dynamic approach where the timetable can be modified in order to enhance the satisfaction of passengers’ demand. In this paper, demand-oriented scheduling and rescheduling models are formalized in order to propose a dynamic timetable that can be modified to satisfy groups of passengers. More specifically, the assessment is based on a min max method. The proposed approach allows the definition of the train timetables for scheduling rail services along a multiline rail network according to operational constraints related to train capacity, train speed limits, passenger’s train transfers, possible conflict in the track section use, with the main objective to minimize the travel time of a set of passengers’ groups. The timetable resulted by the scheduling problem have been used as the input to a rescheduling model where predefined disturbances have been assumed to some nodes of the network. The proposed models are applied to the real case study of the rail network for high-speed trains in the Northern Italy.

Wheeled mobile robots (WMRs) can navigate in uncontrolled environments with the assistance of electronic or physical devices. Several works have been conducted on the control and management of the path-tracking of a vehicle in different road scenarios. This paper aims to create an asynchronous finite state control law for a WMR. The control law is based on a proportional–integral–derivative controller, and the performance of the proposed model is evaluated in virtual and real environments in two different scenarios. In the first one, the WMR must perform a zig-zag maneuver between obstacles, while the second one involves a double left lane change. In the proposed scenarios, the WMR drives along a path until an obstacle is detected at less than 50 cm, causing the WMR to check whether the first lane is free to go and move on. These scenarios and the related required engineering approaches seem particularly suitable for system engineering in a student’s laboratory for the design and implementation of automated guidance system modeling.

A human machine interface (HMI) is presented to switch on/off lights according to the head left/right yaw rotation. The HMI consists of a cap, which can acquire the brain’s electrical activity (i.e., an electroencephalogram, EEG) sampled at 500 Hz on 8 channels with electrodes that are positioned according to the standard 10–20 system. In addition, the HMI includes a controller based on an input–output function that can compute the head position (defined as left, right, and forward position with respect to yaw angle) considering short intervals (10 samples) of the signals coming from three electrodes positioned in O1, O2, and Cz. An artificial neural network (ANN) training based on a Levenberg–Marquardt backpropagation algorithm was used to identify the input–output function. The HMI controller was tested on 22 participants. The proposed classifier achieved an average accuracy of 88% with the best value of 96.85%. After calibration for each specific subject, the HMI was used as a binary controller to verify its ability to switch on/off lamps according to head turning movement. The correct prediction of the head movements was greater than 75% in 90% of the participants when performing the test with open eyes. If the subjects carried out the experiments with closed eyes, the prediction accuracy reached 75% of correctness in 11 participants out of 22. One participant controlled the light system in both experiments, open and closed eyes, with 100% success. The control results achieved in this work can be considered as an important milestone towards humanoid neck systems.

This paper presents a cooperative distributed model for the control of product flow in a network of cooperative inventory systems (ISs). In each IS, a linear input/output equation describes the balance between product demand vs production, as well as the product flows among ISs. The peculiarity of the problem is that both the production and demand are stochastic and cannot be controlled. In addition, each IS has just deterministic information on its inventory level (i.e., the quantity of stored product) which is not shared in the network. Other information is available in each IS regarding the forecast of both its related production and demand. The goal of the proposed model, on the overall network, is to keep both the product stored in each IS and the product exchanged among ISs, around given values as planned a priori. The only available control is related to the product flow between neighboring ISs, which has to be locally computed. Section II describes the proposed approach, which is based on the dual decomposition of the problem, which enables reaching the optimal control. This problem can represent the abstraction of a series of problems, which are not just related to logistics. As examples, section III presents two case studies: a network of virtual power utilities; and the control of risk in the transport of dangerous goods by road.

In recent decades, climate change and a shortage of resources have brought about the need for technology in agriculture. Farmers have been forced to use information and innovation in communication in order to enhance production efficiency and crop resilience. Systems engineering and information infrastructure based on the Internet of Things (IoT) are the main novel approaches that have generated growing interest. In agriculture, IoT solutions according to the challenges for Industry 4.0 can be applied to greenhouses. Greenhouses are protected environments in which best plant growth can be achieved. IoT for smart greenhouses relates to sensors, devices, and information and communication infrastructure for real-time monitoring and data collection and processing, in order to efficiently control indoor parameters such as exposure to light, ventilation, humidity, temperature, and carbon dioxide level. This paper presents the current state of the art in the IoT-based applications to smart greenhouses, underlining benefits and opportunities of this technology in the agriculture environment.

The increase in intermittent renewable energy resources and distributed generation has led to the need for developing new controllers and management techniques for smart grids [...]

The sustainable agriculture cultivation in greenhouses is constantly evolving thanks to new technologies and methodologies able to improve the crop yield and to solve the common concerns which occur in protected environments. In this paper, an MPC-based control system has been realized in order to control the indoor air temperature in a high efficiency greenhouse. The main objective is to determine the optimal control signals related to the water mass flow rate supplied by a heat pump. The MPC model allows a predefined temperature profile to be tracked with an energy saving approach. The MPC has been implemented as a multiobjective optimization model that takes into account the dynamic behavior of the greenhouse in terms of energy and mass balances. The energy supply is provided by a ground coupled heat pump (GCHP) and by the solar radiation while the energy losses related to heat transfers across the glazed envelope. The proposed MPC method was applied in a smart innovative greenhouse located in Italy, and its performances were compared with a traditional reactive control method in terms of deviation of the indoor temperature in respect to the desired one and in terms of electric power consumption. The results demonstrated that, for a time horizon of 20 h, in a greenhouse with dimensions 15.3 and 9.9 m and an average height of 4.5 m, the proposed MPC approach saved about 30% in electric power compared with a relay control, guaranteeing a consistent and reliable temperature profile in respect to the predefined tracked one.

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.

Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyzed traditional versus model predictive control methods with the aim to enhance indoor microclimate condition management under an energy-saving approach. We also reviewed applications of sustainable approaches to reach nearly zero energy consumption, while achieving nearly zero water and pesticide use.

In the last few years, one of the most important challenges of power technologies has been the integration of traditional energy production systems and distributed energy resources. Large-scale photovoltaic systems and wind farms may decrease the quality of the electrical grid service, mainly due to voltage and frequency peaks and fluctuations. Besides, new functionalities, such as the operation in islanded mode of some portions of the medium-voltage grid, are more and more required. In this respect, a model predictive control for voltage and frequency regulation in interconnected local distribution systems is presented. In the proposed model, each local system represents a collection of intelligent buildings and microgrids with a large capacity in active and reactive power regulation. The related model formalization includes a linear approximation of the power flow equations, based on stochastic variables related to the electrical load and to the production from renewable sources. A model predictive control problem is formalized, and a closed-loop linear control law has been obtained. In the results section, the proposed approach has been tested on the Institute of Electrical and Electronics Engineers(IEEE) 5 bus system, considering multiple loads and renewable sources variations on each local system.