Artificial Intelligence Assisted Adaptive Controller Design with Position Robustness in a GaN-based Wireless Power Transfer System

This paper develops an artificial-intelligence (AI)-assisted design approach for adaptive proportional-integral (PI) controllers to optimize control performance of wide band gap (WBG)-based wireless power transfer (WPT) systems. Traditional PI controllers in WPT systems are designed for static operating conditions, struggling with the dynamic nature of varying air gap distances and operating conditions. Our proposed methodology overcomes these challenges by leveraging machine learning and meta-heuristic algorithms, achieving robustness towards varying coil positions and operating conditions. The effectiveness of the adaptive PI controller has been validated through simulation experiments and has demonstrated great improvements over conventional fixed PI controllers.