Model Predictive Control for GaN Power Transistors: A Real-Time Implementation
Imran Sato
Corresponding
National University of Sciences and Technology
Abstract
Scarce measured data and high experimental costs have hindered progress on GaN power transistors in ready-made garment factories. Boundary conditions were derived from in-situ surveys of ready-made garment factories. Accuracy improved by 24%, with the largest gains observed under high-load conditions. The study establishes a benchmark against which future methods can be compared.
Keywords:
GaN power transistorsmodel predictive controlpower qualityseismic performanceconvolutional neural networksgenetic algorithm
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