Optimized sensorless control systems for cargo movement mechanisms

THE PURPOSE. Investigation of the control system of the cargo movement mechanism when using different variants of sensorless control. The search for the optimal option, in which the formation of the speed is identical to the data obtained from the speed sensor. Analysis of the results obtained during the study, including the results obtained taking into account the heating of the motor windings. METHODS. The tasks set during the research are implemented by simulation modeling using the Matlab Simulink computer simulation environment. RESULTS. The article considers systems with different types of velocity observers. A system is implemented that takes into account the heating of the stator and rotor windings of an asynchronous motor, in which a non-adaptive observer and different types of neural network controller were introduced. A combined method of using neural network regulators is proposed. CONCLUSION. Sensorless control systems are relevant for use in industries with the presence, according to the conditions of the technological process, of high temperatures. The conducted research has shown that the use of neural network technologies allows you to work with settings of different levels and types. The proposed method, implying the use of joint work of neural network observers with various neurostructures, allows for speed testing in the entire range. The connection with cloud storage present in the proposed structure leads to the unloading of the management system, allowing to increase the process of analyzing data coming from the object.publishe

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system. Document type: Articl

Autonomous and decentralised energy markets in smart DC microgrids

Dissertation (MEng (Electrical Engineering))--University of Pretoria, 2022.Microgrids are gaining popularity due to their ability to integrate distributed renewable energy generation. In addition, direct current (DC) - based operation results in significantly higher operational efficiency. However, it exhibits energy drawbacks such as congestion, instability, and imbalances. Incorporating demand management through electricity markets governed by dynamic pricing presents a potential solution to these challenges. Concerns about unfair electricity pricing and uneven market power hinder electricity market adoption. This research aims to facilitate decentralised and transparent energy markets with a high-accuracy dynamic pricing scheme to address the critical arguments against current electricity markets.Electrical, Electronic and Computer EngineeringMEng (Electrical Engineering)Unrestricte

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications