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Mobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an entirely new level. Indeed, Cooperative, Connected and Automated Mobility enables and provides ITS services with better Quality of Service (QoS), compared to the same ITS services by only one of the ITS sub-systems (personal, vehicle, roadside, and central, infrastructures), thus improving the road management, reducing congestion, and contributing to sustainable and eco-mobility. By leveraging a network of Smart Infrastructures, it is possible to be continuously and promptly aware about the circulation and environment conditions, as well as the status of connected devices, along with the related technological services. Such knowledge, gained via the adoption of advanced sensing/communication technologies, has the potential to fundamentally shift the mobility paradigm towards mobility as a service. This contributes to more safe, efficient, and comfortable transportation systems. Along this line, information is continuously communicated/shared to vehicles and travellers thanks to dedicated communication services, thus enabling mobility automation and control. Different services - such as providing information about traffic light signal phases and their predicted changes or barriers on the route in realtime- support smooth and comfortable traveling by avoiding strong accelerations/decelerations, by reducing fuel/energy consumption of vehicles with favoured effects on lowering noise and emissions. In this perspective, the special section aims at exploring how to face Coordination and Cooperation challenges for autonomous vehicles in this new connected environment, also in the transition phase where connected human-driven vehicles are present.

This paper addresses the control problem of heterogeneous uncertain nonlinear autonomous vehicle platoons in the presence of adversarial threats arising in Vehicular Ad-hoc NETworks (VANET) during the information sharing process. As unpredictable faults and/or malicious attacks may affect the trustworthiness of the messages shared among vehicles, a suitable resilient control law, able to enhance the robustness of the platoon formation, is required for the prevention of dangerous events. With the aim of achieving a safe platoon control, we leverage Multi-Agent System (MAS) framework and we design a novel distributed backstepping finite-time control strategy, embedding adaptive mechanisms able to guarantee vehicles fleet resilience with respect to possible occurring faults. The proposed strategy falls into the passive fault-tolerant control framework and, hence, it does not require additional observers for fault detection and isolation, thus reducing the computational burden. Adaptive mechanisms are designed according to Lyapunov-based theory which, in combination with the Barbalat lemma, ensures the stability of the closed-loop vehicular network. More specifically, our approach allows guaranteeing the convergence towards zero of the spacing and speed errors, while ensuring that all adaptive signals are bounded in a finite-time interval. A detailed simulation analysis, including a comparison w.r.t. the technical literature, confirms the theoretical derivation, the effectiveness and the advantages of the proposed resilient control law in ensuring platoon formation for different driving scenarios despite the occurrence of unexpected faults.

Due to its extensive applications in different contexts, moving target tracking has become a hot topic in the last years, above all in the military field. Specifically, missile tracking research received a great effort, mainly for its importance in terms of security and safety. Herein, traditional solutions, e.g. Interacting Multiple Model (IMM) based on the Kalman estimation theory, achieve good performance under the main restrictive assumption of the a priori knowledge of the target model, so neglecting the unavoidable presence of model uncertainties and limiting the achievable tracking accuracy only by the presence of the measurement noise. With the specific aim of overcoming this narrowness, this work investigates the capability of deep neural networks in predicting the missile maneuvering trajectories in a model-free fashion. The idea is to leverage the Long-Short Term Memory (LSTM) net due to its excellent capability in learning long-term dependencies of temporal information. Two different LSTM-based architectures have been hence designed to predict both position and velocity of a missile using raw and noisy measurements provided by a realistic radar system, exploiting a large database abundant of realistic off-line data. Training results and theoretical derivations are verified through non-trivial scenarios in order to assess the capability of predicting unknown and realistic 3D missile maneuvers. Finally, the proposed approach has been also compared with a performing model-based IMM algorithm, suitably tuned to deal with realistic missile maneuvers, confirming the excellent generalization abilities of the developed data-driven architectures for different datasets.

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

Abstract The ever increasing demand in passenger and freight transportation is leading to the saturation of railway network capacity. Virtual Coupling (VC) has been proposed within the European Horizon 2020 Shift2Rail Joint Undertaking as a potential solution to address this problem. It allows to dynamically connect two or more trains in a single convoy, thus reducing headway between them. In this work, we investigate the main challenges related to the potential deployment of VC in railways. Its feasibility through Reinforcement Learning techniques is explored, discussing about technical implementation and performance issues. A qualitative analysis based on a Deep Deterministic Policy Gradient control algorithm is proposed. The aim is to give a first insight towards the definition of a qualitative and technology roadmap which could lead to the deployment of artificial intelligence applications aiming at enhancing rail safety and automation.

Optimising energy autonomy for autonomous electric vehicles is a significant challenge in sustainable and environmentally friendly mobility. To this end, we propose a novel double-layer control architecture designed to drive the longitudinal motion of electric vehicles equipped with a regenerative braking system. The first layer resembles an Adaptive Cruise Control, while the second one is enabled during the braking phase and is devoted to the blending between the motor torque and the hydraulic one. This layer is synthesised as a Nonlinear Model Predictive Control strategy so as to maximise the efficiency of the regenerative system. The control architecture, by combining the two control strategies, allows to reduce the overall energy consumption for electric vehicles, while simultaneously ensuring a safer, more sustainable, and comfortable driving experience. Additionally, this combination is capable of compensating for any efficiency issues arising from external factors, such as different traffic conditions encountered by the vehicle. To evaluate the effectiveness of the proposed control architecture, extensive simulation analysis was carried out using the MiTraS virtual testing platform, considering two realistic driving scenarios. Results confirm the robustness and safety of the proposed control architecture in ensuring the tracking of the desired trajectory while optimising the energy consumption.

In this paper a Deep Reinforcement Learning algorithm, known as Deep Deterministic Policy Gradient (DDPG), is applied to the problem of designing a missile lateral acceleration control system. To this aim, the autopilot control problem is recast in the Reinforcement Learning framework, where the environment consists of a 2-Degrees-of-Freedom nonlinear model of the missile’s longitudinal dynamics, while the agent training procedure is carried out on a linearized version of the model. In particular, we show how to account not only for the stabilization of the longitudinal dynamic, but also for the main performance indexes (settling-time, undershoot, steady-state error, etc.) in the DDPG reward function. The effectiveness of the proposed DDPG-based missile autopilot is assessed through extensive numerical simulations, carried out on both the linearized and the fully nonlinear dynamics by considering different flight conditions and uncertainty in the aerodynamic coefficients, and its performance is compared against two model-based control strategies in order to check the capability of the proposed data-driven approach to achieve prescribed closed-loop response in a completely model-free fashion.

In recent years, advancements in Intelligent and Connected Vehicles (ICVs) have led to a significant increase in the amount of information to the driver through Human–Machine Interfaces (HMIs). To prevent driver cognitive overload, the development of Adaptive HMIs (A-HMIs) has emerged. Indeed, A-HMIs regulate information flows by dynamically adapting the presentation to suit the contextual driving conditions. This paper presents a novel methodology, based on multi-objective optimization, that offers a more generalized design approach for adaptive strategies in A-HMIs. The proposed methodology is specifically tailored for designing an A-HMI that, by continuously monitoring the Driver–Vehicle–Environment (DVE) system, schedules actions requested by applications and selects appropriate presentation modalities to suit the current state of the DVE. The problem to derive these adaptive strategies is formulated as an optimization task where the objective is to find a set of rules to manage information flow between vehicle and driver that minimizes both the driver’s workload and the queuing of actions. To achieve these goals, the methodology evaluates through two indexes how applications’ requests impact the driver’s cognitive load and the waiting queue for actions. The optimization procedure has been solved offline to define adaptive strategies for scheduling five application requests, i.e., forward collision warning, system interaction, turn indicators, infotainment volume increase, and phone calls. A theoretical analysis has demonstrated the effectiveness of the proposed framework in optimizing the prioritization strategy for actions requested by applications. By adopting this approach, the design of rules for the scheduling process of the A-HMI architecture is significantly streamlined while gaining adaptive capabilities to prevent driver cognitive overload.

Abstract This paper addresses the time‐varying output formation‐containment control problem for homogeneous high‐order linear Multi‐Agent Systems (MASs). In this context, a PID‐like output‐feedback control strategy is proposed capable to guarantee that all followers reach and maintain a time‐varying formation reference behaviour, as imposed by the multiple leaders, while keeping a prescribed offset with respect to it. Moreover, the proposed approach allows dealing with both communication impairments and external disturbances acting on agents dynamics. The asymptotic stability of the closed‐loop MAS is analytically proved by means of a Lyapunov–Krasovskii approach. Stability conditions are expressed as a feasibility problem with Linear Matrix Inequalities constraints, whose solution provides the proper tuning of the robust PID control gains. Extensive numerical analysis confirms the theoretical framework and discloses the effectiveness of the proposed approach in ensuring the output time‐varying formation‐containment.

Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play a crucial role. Indeed, finding the optimal driving decision in all the different driving scenarios is a challenging task due to the large and complex variability of highway traffic scenarios. In this context, the aim of this work is to design an effective hybrid two-layer path planning architecture that, by exploiting the powerful tools offered by the emerging Deep Reinforcement Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles properly behave in different highway traffic conditions and, accordingly, to determine the lateral and longitudinal control commands. Specifically, the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while the low-level control converts these choices into the lateral and longitudinal vehicle control actions to be imposed through an optimization problem based on Nonlinear Model Predictive Control (NMPC) approach, thus enforcing continuous constraints. The effectiveness of the proposed hierarchical architecture is hence evaluated via an integrated vehicular platform that combines the MATLAB environment with the SUMO (Simulation of Urban MObility) traffic simulator. The exhaustive simulation analysis, carried out on different non-trivial highway traffic scenarios, confirms the capability of the proposed strategy in driving the autonomous vehicles in different traffic scenarios.

In this paper we consider the problem of restoring the voltage for stand-alone inverter-based Microgrids despite the effects of the time-delays arising with the information exchange among the electrical busses. To guarantee that all Distributed Generators (DGs) reach in a finite-time and maintain the voltage set-point, as imposed by a virtual DG acting as a leader, we suggest a novel robust networked-based control protocol that is also able to counteract both the time-varying communication delays and natural fluctuations caused by the primary controllers. The finite-time stability of the whole Microgrid is analytically proven by exploiting Lyapunov-Krasovskii theory and finite-time stability mathematical tools. In so doing, delay-dependent stability conditions are derived as a set of Linear Matrix Inequalities (LMIs), whose solution allows the proper tuning of the control gains such that the control objective is achieved with required transient and steady-state performances. A thorough numerical analysis is carried out on the IEEE 14-bus test system. Simulation results corroborate the analytical derivation and reveal both the effectiveness and the robustness of the suggested controller in ensuring the voltage restoration in finite-time in spite of the effects of time-varying communication delays.

Intradermal therapy, known as mesotherapy, is a technique used to inject a drug into the surface layer of the skin. In particular, it involves the use of a short needle to deposit the drug in the dermis. The intradermal microdeposit modulates the drug’s kinetics, slowing absorption and prolonging the local mechanism of action. It is successfully applied in the treatment of some forms of localized pain syndromes and other local clinical conditions. It could be suggested when a systemic drug-sparing effect is useful, when other therapies have failed (or cannot be used), and when it can synergize with other pharmacological or nonpharmacological therapies. Despite the lack of randomized clinical trials in some fields of application, a general consensus is also reached in nonpharmacological mechanism of action, the technique execution modalities, the scientific rationale to apply it in some indications, and the usefulness of the informed consent. The Italian Mesotherapy Society proposes this position paper to apply intradermal therapy based on scientific evidence and no longer on personal bias.

This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

In recent years, autonomous vehicles and advanced driver assistance systems have drawn a great deal of attention from both research and industry, because of their demonstrated benefit in reducing the rate of accidents or, at least, their severity. The main flaw of this system is related to the poor performances in adverse environmental conditions, due to the reduction of friction, which is mainly related to the state of the road. In this paper, a new model-based technique is proposed for real-time road friction estimation in different environmental conditions. The proposed technique is based on both bicycle model to evaluate the state of the vehicle and a tire Magic Formula model based on a slip-slope approach to evaluate the potential friction. The results, in terms of the maximum achievable grip value, have been involved in autonomous driving vehicle-following maneuvers, as well as the operating condition of the vehicle at which such grip value can be reached. The effectiveness of the proposed approach is disclosed via an extensive numerical analysis covering a wide range of environmental, traffic, and vehicle kinematic conditions. Results confirm the ability of the approach to properly automatically adapting the inter-vehicle space gap and to avoiding collisions also in adverse road conditions (e.g., ice, heavy rain).

The Multiple Microgrids (MMGs) concept has been identified as a promising solution for the management of large-scale power grids in order to maximize the use of widespread renewable energies sources. However, its deployment in realistic operation scenarios is still an open issue due to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, multiple time-varying communication delays consistently appear during data acquisition and the transmission process and their effects must be considered in the control design phase. To this aim, this paper addresses the voltage regulation control problem for MMGs systems in the presence of time-varying communication delays. To solve this problem, we propose a novel hierarchical two-layer distributed control architecture that accounts for the presence of communication latencies in the information exchange. More specifically, the upper control layer aims at guaranteeing a proper and economical reactive power dispatch among MMGs, while the lower control layer aims at ensuring voltage regulation of all electrical buses within each MG to the desired voltage set-point. By leveraging a proper Driver Generator Nodes Selection Algorithm, we first provide the best choice of generator nodes which, considering the upper layer control goal, speeds up the voltage synchronization process of all the buses within each MG to the voltage set-point computed by the upper-control layer. Then, the lower control layer, on the basis of this desired voltage value, drives the reactive power capability of each smart device within each MG and compensates for possible voltage deviations. Simulation analysis is carried out on the realistic case study of an MMGs system consisting of two identical IEEE 14-bus test systems and the numerical results disclose the effectiveness of the proposed control strategy, as well as its robustness with respect to load fluctuations.

The large-scale integration of renewable power generators in power grids may cause complex technical issues, which could hinder their hosting capacity. In this context, the mitigation of the grid voltage fluctuations represents one of the main issues to address. Although different control paradigms, based on both local and global computing, could be deployed for online voltage regulation in active power networks, the identification of the most effective approach, which is influenced by the available computing resources, and the required control performance, is still an open problem. To face this issue, in this paper, the mathematical backbone, the expected performance, and the architectural requirements of a novel decentralized control paradigm based on dynamic agents are analyzed. Detailed simulation results obtained in a realistic case study are presented and discussed to prove the effectiveness and the robustness of the proposed method.

Systems and Synthetic Biology use computational models of biological pathways in order to study in silico the behaviour of biological pathways. Mathematical models allow to verify biological hypotheses and to predict new possible dynamical behaviours. Here we use the tools of non-linear analysis to understand how to change the dynamics of the genes composing a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-engineering and Modelling Assessment (IRMA). Guided by previous theoretical results that make the dynamics of a biological network depend on its topological properties, through the use of simulation and continuation techniques, we found that the network can be easily turned into a robust and tunable synthetic oscillator or a bistable switch. Our results provide guidelines to properly re-engineering in vivo the network in order to tune its dynamics.