This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of $\pm {10}^{\circ }$. Numerical simulations were carried out to validate the control strategy.

Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

The increasing complexity and automation inherent in the contemporary Industry 4.0 paradigm necessitate robust and proactive fault detection methodologies to ensure both operational efficiency and safety. Existing unsupervised anomaly detection techniques, however, often encounter challenges when confronted with the high dimensionality, inherent noise, and complex interdependencies characteristic of industrial data. This paper proposes a novel unsupervised anomaly detection framework explicitly designed for early fault detection within such complex industrial environments. The proposed data-driven methodology systematically identifies the most effective unsupervised model for anomaly prediction from a candidate set of learning algorithms. This approach is particularly advantageous as it obviates the need for labeled historical fault data, a resource often limited in real-world operational settings. The 2015 PHM Data Challenge dataset, specifically selected for its inclusion of systems exhibiting incomplete fault logs, is used to validate the efficacy of the proposed framework. Findings underscore the significant potential of data-driven methodologies to enhance fault detection capabilities, thereby enabling timely intervention and contributing to the improvement of both the reliability and safety of industrial systems.

In this paper, the autonomous trajectory-tracking problem for an Unmanned Aerial Vehicle (UAV) based on Sliding Mode Control (SMC) algorithms is treated. The control system is implemented in real-time to stabilize a commercial AR.Drone 2.0 quadrotor using monocular vision. The under-actuated mathematical model is based on the Newton-Euler formulation. The algorithm allows the stabilization of the quadrotor in all its states under the simultaneous effect of parametric uncertainties and constant external disturbances. The vision algorithm uses a monocular camera to estimate the vehicle’s position. The experimental test results and numerical simulations show the effectiveness and robustness of the proposed controller.