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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.

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.

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.

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.

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.

In this paper, the authors take an amorphous flattened air-ground wireless self-assembling network system as the research object and focus on solving the wireless self-assembling network topology instability problem caused by unknown control communication faults during the operation of this system.

In the paper, the authors propose a neural network-based direct robust adaptive non-fragile fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network integrated system.

The simulation results show that the system eventually tends to be asymptotically stable, and the estimation error asymptotically tends to zero with the feedback adjustment of the designed controller. The system as a whole has good fault tolerance performance and autonomous learning approximation performance. The experimental results show that the wireless self-assembled network topology has good stability performance and can change flexibly and adaptively with scene changes. The stability performance of the wireless self-assembled network topology is improved by 66.7% at maximum.

The research results may lack generalisability because of the chosen research approach. Therefore, researchers are encouraged to test the proposed propositions further.

This paper designs a direct, robust, non-fragile adaptive neural network fault-tolerant controller based on the Lyapunov stability principle and neural network learning capability. By directly optimizing the feedback matrix K to approximate the robust fault-tolerant correction factor, the neural network adaptive adjustment factor enables the system as a whole to resist unknown control and communication failures during operation, thus achieving the goal of stable wireless self-assembled network topology.

The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

There are common problems in the identification of uncertain nonlinear systems, nonparametric approximation, state estimation, and automatic control. Dynamic neural network (DNN) approximation can simplify the development of all the aforementioned problems in either continuous or discrete systems. A DNN is represented by a system of differential or recurrent equations defined in the space of vector activation functions with weights and offsets that are functionally associated with the input data.

This study describes the version of the toolbox, that can be used to identify the dynamics of the black box and restore the laws underlying the system using known inputs and outputs. Depending on the completeness of the information, the toolbox allows users to change the DNN structure to suit specific tasks.

The toolbox consists of three main components: user layer, network manager, and network instance. The user layer provides high-level control and monitoring of system performance. The network manager serves as an intermediary between the user layer and the network instance, and allows the user layer to start and stop learning, providing an interface to indirectly access the internal data of the DNN.

Control capability is limited to adjusting a small number of numerical parameters and selecting functional parameters from a predefined list.

The key feature of the toolbox is the possibility of developing an algorithmic semi-automatic selection of activation function parameters based on optimization problem solutions.

This paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is constructed.

A novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control parameters.

The control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea conditions.

The proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

This paper aims to introduce the quasi impedance source inverter (qZSI)-based static compensator (STATCOM), which is incorporated into the hybrid distributed power generation system for enhancement of power quality. The distributed power generation system includes the photovoltaic (PV), wind energy conversion system (WECS) and battery energy storage system.

The WECS is used by the self-excited induction generator (SEIG) and the flywheel energy storage system (FESS). To regulate its terminal voltage and frequency, the SEIG requires adjustable volt-ampere reactive (VAR). A combination of a STATCOM and a fixed condenser bank usually serves to satisfy the VAR demand. The maximum correntropy criterion-based adaptive filter technique (AFT) is proposed to control the qZSI-STATCOM and to guarantee that the voltage at the SEIG terminal is harmonic-free while providing non-linear three-phase and single-phase loads.

The coordinated operation of the suggested voltage control and flywheel control systems ensures that load voltage and frequency are retained in their respective values at very low harmonic distortions regardless of wind speed and load variation. The simulation and experimental studies are carried out under different load conditions to validate the efficiencies of the PV-assisted STATCOM.

To improve system stability and minimize total costs, extra load current sensors can also be avoided. This paper proposes to control the SEIG terminal voltage and harmonic elimination in the standalone WECS systems using maximum correntropy criterion-based AFT with a fuzzy logic controller.

The purpose of this study is to improve the control performance of wind energy conversion systems (WECSs) by proposing a new sensorless, robust control strategy based on a Smith predictor active disturbance rejection control (SP-ADRC) associated with a speed/position estimator.

The estimator consists of a sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the permanent magnet synchronous generator (PMSG) rotor position and speed. At the same time, the SP-ADRC is applied to the speed control loop of the variable-speed WECS control system to adapt strongly to dynamic characteristics under parameter uncertainties and disturbances.

Numerical simulations are conducted to evaluate the speed tracking performances under various wind speed profiles. The results show that the proposed sensorless speed control improves the accuracy of rotor speed and position estimation and provides better power tracking performance than a regular ADRC controller under fast wind speed variations.

This paper offers a new approach for designing sensorless, robust control for PMSG-based WECSs.

A new sensorless, robust control is proposed to improve the stability and tracking performance of PMSG-based WECSs. The SP-ADRC control attenuates the effects of parameter uncertainties and disturbances and eliminates the time-delay impact. The sensorless control design based on SMO and PLL improves the accuracy of rotor speed estimation and reduces the chattering problem of traditional SMO. The obtained results support the theoretical findings.

This paper aims to propose a new approach to testing distributed energy resources (DERs) in compliance with the IEEE 1547-2018 standard and describes a new, integrated testing and validation system.

The system is built on the virtual instrumentation paradigm, using acquisition modules to measure physical quantities, while signal processing, including intensive calculations of required parameters, data processing, manipulation and reporting are performed on a computing device.

Intensive laboratory measurements were performed on a laboratory prototype of a microgrid that emulates DERs. The results obtained using the system described were compared with the measurements obtained by the reference instruments. As all the results match, the usability of the system was verified.

This approach to the realization of the testing and validation system has obvious advantages compared to the classical instruments and provides significant flexibility in multiple aspects. First, the system described integrates all the functions of different instruments into one measuring system, making the entire testing and validation process significantly cheaper and faster. Second, the implementation of the system is possible on different computing platforms depending on specific needs. Third, the software implementation of the system functions enables simple upgrading and the introduction of new functions or changes to existing ones according to changes in the standard. Finally, the system described is designed to automatically provide reports on compliance with the standard.

This paper emphasizes the advantages of the proposed approach over classical testing. The value of the paper is reflected in the applicability and practical implications of the proposed and described hardware and software technical solutions.

The purpose of this research is to study and analyze the literature that integrates Lean Six Sigma (LSS) approach with blockchain technology in different sectors for improved quality management.

This study presents a scoping review on the application of integrated LSS and blockchain technology in the manufacturing and healthcare sector. Further, the authors examined existing blockchain-based solutions on a variety of dimensions, including application area, technical approach, methodology, application scenario, various blockchain platforms, purpose, and monitoring parameters. The authors study LSS approaches in detail, as well as the key benefits that blockchain technology can enable. Finally, the authors discuss significant research problems to be addressed in order to develop a highly efficient, resilient, and secure quality management framework using blockchain technology.

It has been observed that the adoption of blockchain technology for quality management and assurance is influenced by several factors such as transaction execution speed, throughput, latency. Also, prior blockchain-based solutions have neglected to leverage the benefits of LSS methodologies for effective quality management.

This is the first study to explores the influence of blockchain technology on quality management and assurance in manufacturing and healthcare industry. Furthermore, prior research has not examined how integrating the LSS methodology with blockchain technology can aid in the control of product quality management.

This paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).

First, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.

Comparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.

The problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.