This paper presents a performance-driven adaptive Model Predictive Control (MPC) framework tailored for highfrequency
power converters. By integrating offline optimization with supervised learning, our approach automatically tunes the MPC
parameters to optimize closed-loop performance metrics—specifically, settling time and overshoot. A lightweight, quantization-aware multi-layer perceptron (MLP) is trained to map operating conditions and user-defined performance weights to the optimal weighting matrices in quadratic loss function, enabling rapid, on-the-fly parameter adaptation without incurring online training overhead. The entire framework is implemented on an FPGA using High-Level Synthesis, achieving sub-microsecond inference latency necessary for GaN-based converters operating at MHz-level switching frequencies. Experimental validation on a 12V-3.3V, 1MHz Buck converter demonstrates reduction in voltage overshoot and improvement in settling time compared to fixed-parameter MPC, while maintaining robust performance under varying load conditions. This work effectively bridges model-based control and data-driven learning, offering a promising solution for real-time adaptive control in high-performance power electronics.

FPGA(Field－Programmable Gate Array),Controller Learning,Adaptive MPC,GaN-based DC-DC Converter