Abstract
Predictive maintenance (PDM) is emerging as a strong transformative tool within Industry 4.0, enabling significant improvements in the sustainability and efficiency of manufacturing processes. This in-depth literature review, which follows the PRISMA 2020 framework, examines how PDM is being implemented in several areas of the manufacturing industry, focusing on how it is taking advantage of technological advances such as artificial intelligence (AI) and the Internet of Things (IoT). The presented in-depth evaluation of the technological principles, implementation methods, economic consequences, and operational improvements based on academic and industrial sources and new innovations is performed. According to the studies, integrating CDM can significantly increase machine uptime and reliability while reducing maintenance costs. In addition, the transition to PDM systems that use real-time data to predict faults and plan maintenance more accurately holds out promising prospects. However, there are still gaps in the overall methodologies for measuring the return on investment of PDM implementations, suggesting an essential research direction.

Abstract
Encouraging pre-university students to pursue engineering courses at the university level is essential to meet the industry’s escalating demand for engineers. Each year, universities host hundreds of secondary students who tour their facilities to get a feel for the academic environment. This paper discusses an educational experiment designed as part of a semester-long undergraduate project in Informatics Engineering. The project involves tailoring a Dobot Magician robot, equipped with a standard webcam, to engage in a game of tic-tac-toe against a human user. The camera stream is continuously processed by a computer vision algorithm to detect the pieces placement in the game board. The paper outlines the project development stages, the elements involved, and presents preliminary test results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Abstract
With the global population on the rise and a declining agricultural labor force, the realm of robotics research in agriculture, such as robotic manipulators, has assumed heightened significance. This article undertakes a comprehensive exploration of the latest advancements in controllers tailored for robotic manipulators. The investigation encompasses an examination of six distinct controller paradigms, complemented by the presentation of three exemplars for each category. These paradigms encompass: (i) adaptive control, (ii) sliding mode control, (iii) model predictive control, (iv) robust control, (v) fuzzy logic control and (vi) neural network control. The article further introduces and presents comparative tables for each controller category. These controllers excel in tracking trajectories and efficiently reaching reference points with rapid convergence. The key point of divergence among these controllers resides in their inherent complexity.

Abstract

Abstract
This paper aims to provide an overview of the impact of the COVID-19 pandemic on control engineering education worldwide. The authors, who are educators in the control education field from various countries across all continents, first summarize their experiences to present a global perspective on the different solutions adopted in control education during the pandemic. Afterwards, collected information from the international community through a questionnaire enabled insightful comparisons between pre-pandemic and during-pandemic educational resources and methods, which are shared in this paper. The feedback from the authors’ experiences, along with the questionnaire responses, serves as a valuable resource for learning and improving teaching activities. The questionnaire was distributed among the international control engineering community in collaboration with the International Federation of Automatic Control (IFAC) to explore the diverse alternatives employed globally for conducting online educational activities during the pandemic. These activities include methodologies, tools, theoretical exercises, laboratory experiments, exam types, simulators, and software for online lecturing.