A Comparison of Low-Complexity Iterative Algorithms for Adaptive Active Gate Drivers

Adaptive Active Gate Drivers (AGDs) improve the trade-off between switching losses and transient overshoots by dynamically shaping the gate current. However, their implementation requires the optimization controller to run on limited computational resources while coping with fast load variations. This work compares three low-complexity algorithms for adaptive AGDs, analyzing their computational cost and convergence behavior. The algorithm performance are evaluated on a real AGD test case, showing that steepest gradient descend provides the best trade-off between speed and complexity.