The Virtual Coupling (VC) control problem becomes more challenging when considering High-Speed Trains (HST), where unexpected/unpredictable external factors, nonlinearities effects and unavoidable communication impairments have a stronger impact on trains convoy performance and safety because of the higher velocity operative range. To simultaneously handle all these critical aspects in a unified framework, this paper proposes a novel robust distributed time-delay control strategy which ensures the VC of autonomous connected HST under variable inter-train spacing policy which accounts for the effective convoy conditions. This is also guaranteed in the presence of variations in the active communication links as a result of cooperative manoeuvres. By exploiting matrix theory and the Lyapunov-Krasovskii approach, we derive the control gains tuning procedure and the exponential stability conditions, which are expressed as a set of feasible delay-dependent Linear Matrix Inequalities. These latter allow analytically evaluating the stability margin of the VC convoy with respect to uncertainties, unmodeled dynamics and the maximum allowable delay upper bound, as well as ensuring, based on a desired exponential decay rate, that the closed-loop state trajectories remain bounded within a safe region. Theoretical derivation is confirmed by an extensive simulation campaign, also including different railways cooperative maneuvers, Monte Carlo analysis and comparative evaluation against an alternative strategy.

Since distributed control theory has now become a key ingredient in modern cyber-physical microgrids (MG), its implementation is inseparable from data communication. The introduction of a communication infrastructure inevitably brings communication threats, such as denial-of-service (DoS) attacks and network induced delays. This article represents the first attempt toward a unified distributed digital predictor-based control scheme for the solution of the secondary voltage restoration problem in islanded MGs, which also involves external unknown disturbances and network vulnerabilities, thus enhancing its resilience and reliability. The novel sampled-data predictive controller revises the conventional model reduction approach to reformulate it in a fully distributed digital way and involves some external disturbances information for prediction performance improvement, even though these perturbations are completely unknown. The main features of the resulting method are: 1) large delays compensation accounting for networked-induced delays and sleeping time interval due to DoS attacks occurrence and 2) unknown disturbance attenuation, usually neglected in the controllers synthesis phase in MGs field. Lyapunov-Krasovskii theory is exploited to analytically prove the exponential stability of the MG voltage, thus leading to linear matrix inequality-based sufficient stability conditions. Numerical and experimental results confirm theoretical derivations.

This article extends the problem of safety of the intended functionality (SOTIF) to the platooning of connected autonomous vehicles (CAVs). In such multivehicle systems, SOTIF objectives are particularly challenging, since new issues arise due to the presence of interactions that propagate and intensify the effects and the risk connected to faults/failures/uncertainties, triggered by any platoon member and external conditions. To tackle and solve the SOTIF-oriented platoon control problem, we propose a robust and resilient decentralized control protocol, based on distributed observer which exploits the information shared via the Vehicle-to-Everything (V2X) communication network. The approach allows maintaining acceptable SOTIF performance in adversarial conditions, mitigating the effects of the unreasonable risks originated from sensors/actuators performance limitations/faults and disturbances. The uniformly ultimate bounded (UUB) stability and the input-to-state string stability (ISSS) of the platoon dynamics are analytically proved via the Lyapunov theory. Feasible linear matrix inequalities (LMIs) are obtained as stability conditions, whose solution allows adapting the distributed control/observer gains according to leader-tracking performance and robustness margins. A detailed analysis via the vehicular co-simulation platform mixed traffic simulator (MiTraS) corroborates the theoretical derivations and shows the achievement of the SOTIF platoon goals for a set of nontrivial and uncertain driving scenarios.

This article tackles secondary voltage recovery problem in islanded microgrids with the aim of reducing communication frequency among distributed generation (DG) units, while maintaining desired performance and saving communication network workload. To pursue this objective, a distributed proportional-integral-derivative controller is first introduced, whose sampled-data implementation is enabled by leveraging the finite-difference approximation for the derivative action, which leads to a distributed proportional-integral-retarded (PIR) controller with a small enough sampling period . Then, the resulting fully distributed PIR control law is combined with a dynamic event-triggered mechanism (DETM), which embeds Zeno-freeness property and avoids the requirement of continuous transmission in triggering process. Thus, the communication burden is significantly mitigated and the waste of communication resources is avoided. By exploiting Lyapunov-Krasovkii method, we derive exponential stability conditions expressed as linear matrix inequalities (LMIs), whose solution allows evaluating the maximum sampling period and DETM parameters preserving the stability of the microgrid. A thorough numerical analysis, carried out on the standard IEEE 14-bus test system, confirms the theoretical derivation.

This work deals with the design of a control system enabling the autonomous driving of Grade of Automation 4 (GoA4) for high-speed trains over ETCS. GoA4 requires trains to autonomously adapt their behaviour to the different driving conditions occurring in real-world trips and involving a wide range of possible maneuvers, even in the presence of unexpected events forcing ETCS interventions. To ensure this high automation level, we propose a hierarchical, modular and scalable control architecture, named Autonomous Driving Function (ADF). It embeds the main ATO functionalities, i.e., optimization of the recommended speed profile and train speed tracking, both nontrivial tasks due to train nonlinear dynamics and complex external environment, while being still compliant with railway standards. Hence, ADF is devoted to the real-time trajectory planning, considering the actual data acquired and the current driving situation along with the related on-line constraints, and to the trajectory tracking which, realized via different classes of controllers, ensures a safe and efficient train motion, also compliant with the railway standard. ADF is designed according to the Model Based Control Design (MBCD) approach which fully covers the V-Cycle development process and supports automatic C code generation compliant to standard EN50128, early design validation, testing, simulation and run-time verification. Finally, thanks to the experimental validation, carried out on an inspection high speed prototype vehicle, ADF has the prospect of becoming an inherent architecture for guaranteeing the GoA4 autonomous diving for HST over ETCS.

In this work, the leading-tracking control problem for heterogeneous and uncertain nonlinear autonomous vehicles is addressed. These latter communicate their status information over a wireless communication network and engage in cooperative movements in extra-urban traffic environment. The nonlinear vehicle dynamics are affected by time-varying parameter uncertainties, and the air-drag reduction caused by platooning is explicitly considered. The variation of V2V links as a result of cooperative manoeuvres is modelled by incorporating switching into the communication network topology. To this aim, we develop a novel robust distributed Proportional-Integral-Derivative (PID)-like control protocol which ensures that all vehicles within the platoon robustly track the behaviour of the leader, despite the presence of both unknown parameters uncertainties and network switching. The analytical stability of the proposed control strategy is demonstrated using Lyapunov theory. Sufficient stability conditions are expressed as a set of feasible Linear Matrix Inequalities (LMIs) whose solution allows the robust control gains to be properly tuned. The effectiveness of the approach is evaluated via the Mixed Traffic Simulator (MiTraS) co-simulation platform, which combines MATLAB/Simulink with SUMO microscopic traffic simulator. The exhaustive simulation analysis, involving the Monte Carlo method, is carried out considering different cooperative platooning manoeuvres, and confirms the theoretical derivation.

Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing’s condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method’s capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches.

Internet of Vehicle (IoV) has become a hot topic in the last years due to its significant benefits in transportation systems in terms of quality, costs, and traffic management capability. Indeed, accurate and real-time information acquisition and diffusion are required to reach an efficient traffic management system according to the smart city paradigm. In this context, this article provides a novel IoV-based distributed double layer control architecture to address the coordination control problem of connected autonomous vehicles (CAVs) multiple platoons negotiating the access to a road section restriction in the presence of heterogeneous communication time-varying delays. To this aim, leveraging the IoV paradigm and the equivalent virtual formation concept, we propose a novel distributed diffusive longitudinal protocol and a distributed lateral potential-function-based control strategy, where both of them are able to cope with communication delays and ensuring the multiplatoon coordination in finite time, i.e., before accessing the road section restrictions, so to avoid vehicles collisions. The finite-time stability of both the cooperative controllers is analytically proved by exploiting the Lyapunov–Krasovskii theory and the derived delay-dependent stability conditions, expressed as a set of linear matrix inequalities (LMIs), allows the proper tuning of the controllers gains. Numerical analysis confirms the theoretical derivation and discloses the effectiveness and the robustness of the proposed cooperative multiplatoon control in ensuring the safe crossing of the road section restriction in finite time.

The improvement of energy performance for platoons of autonomous connected vehicles is one of the major challenges the road transport sector is facing with. To this aim, this work addresses and solves the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon via a novel Eco-Driving Control Architecture able to optimize its energy consumption performance while ensuring the fulfillment of the optimal leader tracking trajectory. Specifically, it consists of a Nonlinear Model Predictive Control (NMPC) strategy, driving the leader motion and computing the optimal ecological trajectory to be imposed on the whole platoon, and a novel distributed exponentially-stable robust PID-like protocol, driving the follower vehicles motion for achieving a precise leader-tracking with a desired transient behavior as required for the accurate implementation of the energy-saving control. The exponential stability of the overall vehicular network is analytically proven with the Lyapunov theory and the derived robust stability conditions allow the proper tuning of the control gains on the basis of the desired decay rate. The efficiency of the proposed approach is corroborated via the high-fidelity Mixed Traffic Simulator (MiTraS) co-simulation platform under different operative scenarios and a wide uncertainty range for the vehicles parameters. Simulation results confirm how the proposed architecture ensures the eco-driving behaviour for the whole vehicles platoon.

In this article, the control approach based on structured input–output finite-time stability (IO-FTS) is extended to tackle the reference tracking problem. IO-FTS was originally introduced to deal with the disturbance rejection problem. By applying the finite-time stability control to a properly augmented system, we show that it is possible to enforce a set of specific requirements on the response of the closed-loop system during the transients, taking also into account saturation constraints on the actuators. Adding IO-FTS constraints to classic-state-feedback control law leads to a feasibility problem with bilinear matrix inequality (BMI) constraints. Herein, we show how the original BMI problem can be relaxed to a linear matrix inequality (LMI) one, which comes at a price of more conservatism, but turns out to be computationally more efficient. To prove the effectiveness of the proposed approach, we consider the case of the longitudinal control of a missile.

The development of autonomous connected vehicles, moving as a platoon formation, is a hot topic in the intelligent transportation system (ITS) research field. It is on the road and deployment requires the design of distributed control strategies, leveraging secure vehicular ad-hoc networks (VANETs). Indeed, wireless communication networks can be affected by various security vulnerabilities and cyberattacks leading to dangerous implications for cooperative driving safety. Control design can play an important role in providing both resilience and robustness to vehicular networks. To this aim, in this article, we tackle and solve the problem of cyber-secure tracking for a platoon that moves as a cohesive formation along a single lane undergoing different kinds of cyber threats, that is, application layer and network layer attacks, as well as network induced phenomena. The proposed cooperative approach leverages an adaptive synchronization-based control algorithm that embeds a distributed mitigation mechanism of malicious information. The closed-loop stability is analytically demonstrated by using the Lyapunov-Krasovskii theory, while its effectiveness in coping with the most relevant type of cyber threats is disclosed by using PLEXE, a high fidelity simulator which provides a realistic simulation of cooperative driving systems.

Autonomous driving is a safety critical application of sensing and decision-making technologies. Communication technologies extend the awareness capabilities of vehicles, beyond what is achievable with the on-board systems only. Nonetheless, issues typically related to wireless networking must be taken into account when designing safe and reliable autonomous systems. The aim of this work is to present a control algorithm and a communication paradigm over 5G networks for negotiating traffic junctions in urban areas. The proposed control framework has been shown to converge in a finite time and the supporting communication software has been designed with the objective of minimizing communication delays. At the same time, the underlying network guarantees reliability of the communication. The proposed framework has been successfully deployed and tested, in partnership with Ericsson AB, at the AstaZero proving ground in Goteborg, Sweden. In our experiments, three heterogeneous autonomous vehicles successfully drove through a 4-way intersection of 235 square meters in an urban scenario.

Railway infrastructure operators need to push their network capacity up to their limits in high-traffic corridors. Virtual coupling is considered among the most relevant innovations to be studied within the European Horizon 2020 Shift2Rail Joint Undertaking as it can drastically reduce headways and thus increase the line capacity by allowing to dynamically connect two or more trains in a single convoy. This paper provides a proof of concept of Virtual coupling by introducing a specific operating mode within the European rail traffic management system/European train control system (ERTMS/ETCS) standard specification, and by defining a coupling control algorithm accounting for time-varying delays affecting the communication links. To that aim, we define one ploy to enrich the ERTMS/ETCS with Virtual coupling without changing its working principles and we borrow a numerical analysis methodology used to study platooning in the automotive field. The numerical analysis is also provided to support the proof of concept with quantitative results in a case-study simulation scenario.

Human driver behavior strongly influences traffic flow by increasing the spread of shock waves in a downstream direction. This paper addresses the problem of traffic congestion mitigation in a mixed scenario composed of connected human-driven and autonomous vehicles. The control protocol, driving the longitudinal motion of the autonomous vehicles, is designed for damping down traffic waves. The effectiveness of the strategy, and ability to cope with multiple and time-varying delays originated by the non-ideal wireless communication among connected vehicles, is both analytically and numerically analyzed. The asymptotic stability of the algorithm is mathematically proved by leveraging a Lyapunov-Krasovskii functional, while the head-to-tail stability tool is exploited for the tuning of the control gains. The performance of the control strategy is disclosed by using hardware-in-the-loop real-time simulation for an exemplary pattern of three vehicles. The effectiveness of the proposed cooperative strategy in longer queues of vehicles is, instead, investigated through PLEXE, an inter-vehicular communication and mobility simulator that includes features for autonomous vehicles as well as for the realistic emulation of the IEEE 802.11p standard. The results confirm the robustness of the approach and its ability in damping disturbances and mitigating stop-and-go effects according to the theoretical derivation.

In this paper, we present the cooperative driving system developed by the Chalmers car team for the grand cooperative driving challenge 2016. This paper gives an overview of the system architecture and describes in detail the communication, signal processing, and decision-making sub-systems. Experimental results demonstrate the system's performance and operation according to the rules and requirements of the competition.

The domain of medical decision making process is heavily affected by vagueness and uncertainty issues and - for copying with them - different type of Clinical Decision Support System (CDSS)s, simulating human expert clinician reasoning, have been designed in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the knowledge-based CDSS actually used in the day-by-day clinical care of β-thalassemia patients of the Rare Red Blood Cell Disease Unit (RRBCDU) at Cardarelli Hospital (Naples, Italy). All the designed functionalities were iteratively developed on the field, through requirement-adjustment/development/validation cycles executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The paper shows exemplary results on the on-line evaluation of Iron Overload during the health status assessment and care management of β-Thalassemia patients.

Thanks to resource elasticity, cloud systems allow to build high performance applications by dynamically adapting resources to workload dynamics. In this paper, we present a novel approach for horizontally scaling cloud resources. The approach is based on an optimized feedback control scheme that leverages fuzzy logic to self-adjust its parameters in order to cope with unpredictable and highly time-varying public-cloud operating conditions. The proposed approach takes as input heterogeneous monitoring metrics related to distinct aspects of interest (i.e., CPU and network load) merged through a fitness function. Therefore, it is able to accomplish the application needs from different viewpoints. The extensive experimental evaluation performed in the Amazon EC2 environment showed how the proposed approach is robust against a number of realistic workloads-also when VM failures happen- and that it is flexible, as being suitable for applications with different needs. Finally, it also achieves better performance when compared to previously proposed solutions.

Nowadays, more and more the industry and market depend on cloud-based infrastructures for delivering IT services. To this aim cloud-based infrastructures are changing continuously, increasing their complexity especially for the management of cloud resources. Control and management of resources (e.g., virtual machines, VMs) are of paramount importance to adjust resources automatically allocated to an application and for delivering quality-assured services to final users. In this paper, we propose a feedback-based control approach for the management of VMs in the AWS EC2 public cloud. First, we evaluate the proposed Gain Scheduling policy against different workloads. Second, we provide results on the robustness of the proposed Gain Scheduling policy in presence of failures. Finally, we compare our approach to state-of-the-art control approaches for cloud resources. Our results indicate that the proposed control strategy guarantees, without the need of a priori information on system dynamics or complex estimations of the operating conditions, high performance with respect to both constant and time- varying workloads as well as in spite of sudden VM failures.

Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.

Nowadays smart mobility, a new vision of urban mobility, is a reality. To implement smart mobility scenarios a deep integration among citizens, private and public transportation systems and ICT is required. With the S2-Move project we propose an architecture able to collect, update, and process real-time and heterogeneous information from various sources (tablets, smart-phones, probe vehicles) and actors of the urban scenario (public/private vehicles, pedestrians, infrastructures) in order to provide innovative mobility services. In this paper, we present a core component of the S2-Move project: the map-based web platform designed and implemented for providing smart mobility services. We present use cases and detail the design and the implementation of the platform. Finally, we evaluate the accuracy and efficiency of the Map Matching and Traffic Monitoring algorithms we implemented in our platform by using realistic urban traffic data generated through simulations in SUMO.

We analyze and solve the platooning problem by treating it as the problem of achieving consensus in a network of dynamical systems affected by time-varying heterogeneous delays due to wireless communication among vehicles. Specifically, a platoon is modeled as a dynamical network where: 1) each vehicle, with its own dynamics, is a node; 2) the presence of communication links between neighboring vehicles is represented by edges; and 3) the structure of the intervehicle communication is encoded in the network topology. A distributed control protocol, which acts on every vehicle in the platoon, is derived. It is composed of two terms: a local action depending on the state variables of the vehicle itself (measured onboard) and an action depending on the information received from neighboring vehicles through the communication network. The stability of the platoon is proven by using Lyapunov-Razumikhin theorem. Numerical results are included to confirm and illustrate the theoretical derivation.

In this paper the Cooperative Adaptive Cruise Control strategy for vehicles platooning is extended to the case when each vehicle can communicate with a subset of vehicles in the fleet. The control objective is to guarantee that the fleet moves forward with a given spacing policy at the leader velocity. To this aim each vehicle decides its control action using information from all neighboring vehicles through wireless communication. In so doing, a network of dynamical systems is formed, and it is shown that achieving platooning is equivalent to find a control algorithm so that the resulting network is asymptotically stable. A network protocol able to deal with heterogeneous time-varying communication delays is then proposed to solve the problem. A consistent proof of stability of the closed-loop system is provided and numerical results confirm the effectiveness of the approach and its robustness with respect to variations of the leader velocity, as well as to generic topologies of the underlying network emerging from the communication features.

In this paper we propose an architecture to supervise and operate platoons of vehicles. The resulting system is mainly composed by a decentralized architecture where each vehicle, equipped with an On Board Unit, collects the fundamental data and forwards them to an operation center that stores and reorganizes them. We focus here on the automatons implemented in each system and we show a first prototype for its experimental validation.

The aim of the paper is to investigate the integration of different analytical and simulations tools to support people to make decisions, and to show how the relationships among the different methods can be advantageous to solve specific problems. ORM (object - role modeling), PN (Petri Nets) and SD (System Dynamics) have been combined to capture the static and dynamic aspects of system.

This paper presents the first digital implementation of a novel model reference adaptive scheme for the control of piecewise affine circuits and systems. The control law is implemented by using a low-cost commercial microcontroller. The aim is to control a piecewise-linear electrical circuit. The experimental validation process is made challenging by the presence of measure uncertainties, noise, quantization errors, unmodelled nonlinear dynamics, computational delays. Moreover, a digital microcontroller is used to implement an analogue continuous-time control law. Nevertheless, experiments confirm the effectiveness of the controller to cope with switching in the circuit dynamics, establishing the strategy as a viable control tool.

This paper is concerned with the design and comparison of a new optimal-adaptive control of an electronic throttle body. Numerical results are complemented by experiments.

This paper is concerned with the investigation of a novel hybrid LQ-adaptive strategy based on the so called minimal control synthesis algorithm (MCS). A model reference MCS adaptive control scheme is implemented where the reference model is controlled via an LQ regulator. An additional supervisory control action updates on-line the reference model and the gains of the LQ control, switching between a control and an identification phase. The resulting hybrid LQ-MCS algorithm is validated on a representative case study.