3 results
Search Results
Now showing 1 - 3 of 3
Article Citation - Scopus: 1Investigation of Harmonic Losses To Reduce Rotor Copper Loss in Induction Motors for Traction Applications(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Siddique, M.S.; Ertan, H.B.; Alam, M.S.; Khan, M.U.The focus of this paper is to seek means of increasing induction motor efficiency to a comparable level to a permanent magnet motor. Harmonic and high-frequency losses increase the rotor core and copper loss, often limiting IM efficiency. The research in this study focuses on reducing rotor core and copper losses for this purpose. An accurate finite element model of a prototype motor is developed. The accuracy of this model in predicting the performance and losses of the prototype motor is verified with experiments over a 32 Hz–125 Hz supply frequency range. The verified model of the motor is used to identify the causes of the rotor core and copper losses of the motor. It is found that the air gap flux density of the motor contains many harmonics, and the slot harmonics are dominant. The distribution of the core loss and the copper loss is investigated on the rotor side. It is discovered that up to 35% of the rotor copper losses and 90% rotor core losses occur in the regions up to 4 mm from the airgap where the harmonics penetrate. To reduce these losses, one solution is to reduce the magnitude of the air gap flux density harmonics. For this purpose, placing a sleeve to cover the slot openings is investigated. The FEA indicates that this measure reduces the harmonic magnitudes and reduces the core and bar losses. However, its effect on efficiency is observed to be limited. This is attributed to the penetration depth of flux density harmonics inside the rotor conductors. To remedy this problem, several FEA-based modifications to the rotor slot shape are investigated to place rotor bars deeper than the harmonic penetration. It is found that placing the bars further away from the rotor surface is very effective. Using a 1 mm sleeve across the stator’s open slots combined with a rotor tapered slot lip positions the bars slightly deeper than the major harmonic penetration depth, making it the optimal solution. This reduces the bar loss by 70% and increases the motor efficiency by 1%. Similar loss reduction is observed over the tested supply frequency range. © 2025 by the authors.Article A Systematic Review of Social Robots in Shopping Environments(Taylor and Francis Ltd., 2025) Khan, M.U.; Erden, Z.Social robots, driven by cutting-edge technologies are designed to cater to various societal needs, facilitating complex human interactions involving multiple users and stakeholders. Their integration into daily life is anticipated to increase significantly. Within this context, the domain of shopping robots, which play a crucial role in enhancing and diversifying shopping experiences where human interaction is paramount, holds immense potential for development. This article aims to provide an overview of the current landscape of shopping robots and explore future research directions in this evolving field. Through this systematic review, key trends and insights in the field of shopping robots are identified, while also offering a categorization in the form of a 3D conceptual scheme, called the Public Space Robot (PSR) framework. The outcomes reveal significant developments over the past two decades (2002–2024), with the main concentration on developing and deploying mobile robots that offer functional or autonomous interaction for navigation assistance and customer service in shopping malls and retail stores. © 2024 Taylor & Francis Group, LLC.Conference Object Reinforcement Learning-Based Multi-Robot Path Planning and Congestion Management in Warehouse Order Picking(Institute of Electrical and Electronics Engineers Inc., 2024) Alam, M.S.; Khan, M.U.; Gunes, A.This paper addresses the multi-robot path planning problem in a warehouse environment using reinforcement learning. The warehouse layout comprises of a grid map with multiple robots for retrieval and delivery of orders, inventory pods for storage, and pick stations for receiving outbound orders. The robots are required to pick and deliver orders from target shelves to their corresponding pick stations by navigating in a complex network of aisles. Q-learning algorithm computes optimal paths for the robots, while avoiding congestion in the aisles. Simulation results demonstrate the efficacy of the proposed method in optimizing both travel time and travel distance, thus enhancing the overall operational efficiency of the warehouse. © 2024 IEEE.
