As industrial cyber–physical systems (ICPSs) consolidate as mature automation solutions, questions about leveraging their flexibility and resilience to maintain performance have arisen. This article presents a methodology that employs industrial agents (IAs) to supervise and reconfigure service-oriented ICPSs, thereby enabling adaptability and improving performance. The main tasks of the IAs include: 1) selecting the best service for the ICPS, from a predefined component library, based on performance metrics and computational load; and 2) monitoring the performance of the chosen service online, carrying out a new selection process if poor performance is detected, thus enabling dynamic service selection. A key contribution is the introduction of a dynamic reconfiguration capability to the system, allowing it to adapt in real-time to changing conditions, thereby addressing a limitation of existing service-oriented architectures. Results show an improvement in system adaptability and performance, demonstrating the potential of agent-based supervision to support the operation of ICPSs and positioning the proposed methodology as an initial step toward the development of more resilient and efficient ICPSs.

In the pursuit of a personalized healthcare experience, data-driven decision-making has become increasingly relevant. In this context, there is a growing need for technological systems specifically tailored to efficiently manage vast amounts of healthcare data. To address this need, in this work, we contribute by presenting a cyber-physical solution for monitoring and analyzing physiological data obtained from wearable devices. The proposed system is designed following a service-oriented architecture, which promotes modularity and enables efficient data access and analysis. The system is capable of ingesting and consolidating wearable data produced by a variety of commercial devices, scaling to accommodate a large number of data producers, and accepting queries from a multitude of consumers through various mechanisms. Performance tests conducted in various scenarios using real data demonstrate the system’s effectiveness in maintaining real-time data access.

This article presents the design and analysis of a switching high-gain adaptive control scheme for a class of nonlinear systems. Adaptation is included in the scheme to estimate the controller gains, using differential equations whose order can switch between 1 (integer order) and some real number (fractional order) in the interval (0, 1), depending on the error level. This switching strategy permits obtaining lower values for controller gains due to fractional orders, resulting in improved robustness, while simultaneously guaranteeing fast convergence of the state to the origin due to the integer order, leading to a better balance between system behavior and control energy efficiency. Applications to multiagent systems are presented to illustrate the potential of the proposed scheme.

Industrial cyber-physical systems (ICPSs) are widely regarded as the next generation industrial control systems and as one of the core technologies of the ongoing fourth industrial revolution. Despite its advantages, ICPSs are heavily dependent on the underlying physical process and their synthesis is a customized effort, demanding in terms of resources, which if not conducted carefully may impact the performance of the system. This work proposes a methodology to tackle ICPS synthesis in a systematic way, by using a set of industrial agents that take as input and standardized process description file and automatically deploy a modular ICPS from predesigned functional containers. Concrete examples on a tanks system and an industrial paste thickener are presented to illustrate the potential of the proposed methodology.

Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes are hybrid closed-loop systems, which require the user to announce every meal and its size. However, estimating the amount of carbohydrates in a meal and announcing each and every meal is an error-prone process that introduces important uncertainties to the problem, which when not considered, lead to sub-optimal outcomes of the controller. To address this problem, we propose a novel deep-learning-based model for probabilistic glucose prediction, called the Input and State Recurrent Kalman Network (ISRKN), which consists in the incorporation of an input and state Kalman filter in the latent space of a deep neural network so that the posterior distributions can be computed in closed form and the uncertainty can be propagated using the Kalman equations. In addition, the proposed architecture allows explicit estimation of the meal uncertainty distribution, whose parameters are encoded in the filter parameters. Results using the UVA/Padova simulator and data from a clinical trial show that the proposed model outperforms other probabilistic models using several probabilistic metrics across different degrees of distributional shifts.

Industrial Cyber-Physical Systems (ICPS) are regarded as the next-generation industrial control systems and as one of the core technologies of the ongoing fourth industrial revolution. Despite their advantages, ICPS present challenges that must be addressed to unleash their full potential. Among them is the standardization of information models. Today, various industry standards are used for information modeling; however, these efforts do not consider all aspects of an ICPS and mainly focus on describing the engineering and logic of the industrial process. This work proposes information modeling based on graphs that take into account the following aspects of an ICPS: engineering, production flow, and feedback control; trying to obtain an information model that integrates the relationships of the different devices in a process both vertically and horizontally. A concrete case study on a mineral processing plant is presented to illustrate the potential of the proposed modeling methodology.

Classical model-based control techniques used in process control applications present a tradeoff between performance and computational load, especially when using complex nonlinear methods. Learning-based techniques that allow the controller to learn policies from data represent an appealing alternative with potential to reduce the computational burden of real-time optimization. This article presents an efficient learning-based neural controller, optimized using evolutionary algorithms, designed especially for maintaining diversity of individuals. The search of solutions is conducted in the parameter space of a population of deep neural networks, which are efficiently encoded with a novel compression algorithm. Evaluation against strong baselines demonstrates that the proposed controller achieves better performance in most of the chosen evaluation metrics. Results suggest that learning-based controllers are a promising option for next-generation process control in the context of Industry 4.0.

Chronotherapy aims to treat patients according to their endogenous biological rhythms and requires, therefore, knowing their circadian phase. Circadian phase is partially determined by genetics and, under natural conditions, is normally entrained by environmental signals (zeitgebers), predominantly by light. Physiological data such as melatonin concentration and core body temperature (CBT) have been used to estimate circadian phase. However, due to their expensive and intrusive obtention, other physiological variables that also present circadian rhythmicity, such as heart rate variability, skin temperature, activity, and body position, have recently been proposed in several studies to estimate circadian phase. This study aims to predict circadian phase using minimally intrusive ambulatory physiological data modeled with machine learning techniques. Two approaches were considered; first, time-series were used to train artificial neural networks (ANNs) that predict CBT and melatonin dynamics and, second, a novel approach that uses scalar variables to build regression models that predict the time of the minimum CBT and the dim light melatonin onset (DLMO). ANNs require less than 48 hours of minimally intrusive data collection to predict circadian phase with an accuracy of less than one hour. On the other hand, regression models that use only three variables (body mass index, activity, and heart rate) are simpler and show higher accuracy with less than one minute of error, although they require longer times of data collection. This is a promising approach that should be validated in further studies considering a broader population and a wider range of conditions, including circadian misalignment.

Being able to detect, identify, and diagnose a fault is a key feature of industrial supervision systems, which enables advance asset management, in particular, predictive maintenance, which greatly increases efficiency and productivity. In this paper, an Industrial Internet app for real-time fault detection and diagnosis is implemented and tested in a pilot scale industrial motor. Real-time fault detection and identification is based on dynamic incremental principal component analysis (DIPCA) and reconstruction-based contribution (RBC). When the analysis indicates that one of the vibration measurements is responsible for the fault, a convolutional neural network (CNN) is used to identify the unbalance or bearing fault type. The application was evaluated in its three functionalities: fault detection, fault identification, and fault identification of vibration-related faults, yielding a fault detection rate over 99%, a false alarm rate below 5%, and an identification accuracy over 90%.

This article presents a real implementation of a neural network-based model predictive control scheme (NNMPC) to control an industrial paste thickener. The implementation is done over an Industrial Internet of Things (IIoT) platform designed using the seven layer reference model for IIoT systems. Modeling is achieved using an encoder-decoder with attention recurrent neural network, while MPC search is done using particle swarm optimization. An industrial evaluation is presented, which highlights the set-point tracking and disturbance rejection capabilities of the proposed NNMPC technique.