Browsing by Author "Kalyankolo, Zaina"

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    Fuzzy-PID control design and performance analysis for PMSM drives in electric vehicles
    (Lamintang Education and Training (LET) Centre, 2025-12-28) Kalyankolo, Umaru; Nafuna, Ritah; Mugabe, Rodney; Nansukusa, Yudaya; Asikuru, Salaama; Ochima, Noah; Mutaburura, Pison; Kalyankolo, Zaina
    The increasing demand for high performance and energy efficient electric vehicles has driven research into advanced motor control strategies for Permanent Magnet Synchronous Motors. This study investigates the design and performance evaluation of a Fuzzy PID controller as the speed regulator to address the limitations of the typical PID controllers in EV propulsion and a field-oriented control strategy is used. A conventional PID controller is initially implemented and tuned using the Ziegler-Nichols closed loop method. A Fuzzy Inference System is developed and then integrated with the PID controller to form a hybrid Fuzzy PID controller capable of adjusting the PID gains in real time. The performance of both controllers is evaluated under various test scenarios including speed variations, load disturbances, and parameter changes. Simulation results demonstrate that the Fuzzy PID controller significantly reduced overshoot by 0.5%, reduced rise time by 32.04%, improved settling time by 8.04%, and therefore enhanced system stability and responsiveness compared to the typical PID controller. These improvements validate the effectiveness of fuzzy logic in managing the uncertainties associated with PMSM control in EV applications.
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    PLC based speed control in a color sorting system: A design and simulation perspective
    (Journal of Engineering, Technology and Applied Science, 2025-04-25) Kalyankolo, Umaru; Munguleni, Marlon; Nansukusa, Yudaya; Asikuru, Salaama; Nafuna, Ritah; Kalyankolo, Zaina
    For simulation purposes in Factory IO and TIA Portal, the research is tailored to emulate industrial sorting operations. In numerous industrial scenarios, sorting operations play a crucial role, with objects segregated based on various criteria such as dimensions, colors, weight, and material composition. For instance, within Thermal Power Stations, electromagnetic sorting techniques are employed to separate ferromagnetic materials from coal. This research specifically focuses on sorting goods based on color, with adjustable speed parameters to match production rates. The system is equipped with a digital display screen, providing real-time feedback on the count of sorted objects, and receives an analog speed signal from the PLC for precise control. This research is divided into two primary components, i.e. Software and Simulation in Factory IO. The software aspect involves the implementation of ladder logic programming in TIA Portal, enabling systematic control of the entire research process based on the input data sequence, while the simulation in Factory IO is encompasses the virtual representation of conveyors for object transportation and RGB color vision sensors for color detection. The entry conveyor features two branches to load objects onto the respective conveyors, directed by the sorting logic implemented in TIA Portal.
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    Predictive maintenance (Ai) in power generation for rotating machines based on vibration analysis
    (2024) Kalyankolo, Zaina; Mwesigwa, Samuel; Ainomuhwezi, Martha; Kibande, Steven; Kalyankolo, Umar
    Predictive maintenance, aided by Artificial Intelligence (AI), has emerged as a game-changing approach that will revolutionize how to manage and maintain machinery especially rotating machinery particularly in power generation equipment. Traditional preventive maintenance approaches have proven to be expensive and time-consuming, and frequently fail to detect possible issues before they occur. Case in point is the Callide Power Station’s Unit C4 incident. In 2021, an offshore platform experienced a catastrophic failure of gas turbine generator due to a sudden bearing failure. The incident took place on May 25, 2021, at the Callide Power Station's Unit C4. This breakdown resulted in substantial damage to the transmission network. The primary cause was discovered as high system vibrations and inadequate maintenance of the lubrication systems, which are critical for the smooth operation of the bearings. This paper presents predictive maintenance (Pd.M.) as a leveraging solution to abrupt failure in rotating machines. The paper focuses on vibration analysis as a major determinant of the equipment health.