Purpose: This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approach: The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.Findings: The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implications: Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/value: The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

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.

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 $h>0$ . 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 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.

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.