2021-10-132021-10-132021-052021-05May 2021https://hdl.handle.net/2346/88084In high power applications, semiconductor power modules containing paralleled MOSFETs are often used to achieve high output currents. The current distribution between devices within a module is influenced by several factors such as component layout, minor variances due to manufacturing tolerances, and general device degradation that occurs over time. This thesis describes a method for balancing the current between paralleled MOSFETs by independently modulating each device’s gate-to-source voltage and measuring the corresponding drain-to-source current. To achieve this, a detailed simulation is created using MATLAB and Simulink. A reinforcement learning agent is implemented with the goal of adaptively balancing power module current as the components inside degrade over time. After extensively simulating different variants of the system along with various hyperparameter combinations, research transitioned to a physical system where similar successful results are achieved.Embargo status: Restricted to TTU community only. To view, login with your eRaider (top right). Others may request access exception by clicking on the PDF link to the left.application/pdfengMOSFETIntelligent Power ModuleCurrent BalancingReinforcement LearningDeep Q-LearningDeep Q-NetworksDQNThesis2021-10-13Restricted to TTU community only.