Deep Learning Assisted -2P2Z Control for Boost DC/DC Converter

Deep Learning (DL) networks have demonstrated exceptional capability in handling complex system dynamics but have traditionally been constrained by the computational limitations of DSPs lacking parallel processing capabilities. With the emergence of next-generation DSPs equipped with Neural Processing Units (NPUs), DL networks can now complement traditional linear controllers by addressing the nonlinear characteristics of power electronic systems in real time. This study proposes an online-learning DL-assisted 2 Poles − 2 Zeros (2P2Z) controller for a boost converter, aiming to enhance transient and steady-state performance across varying operating conditions. In particular, the proposed approach mitigates the adverse effects of the Right-Hand-Plane (RHP) zero, improving system stability and dynamic response. The effectiveness of the design is validated through continuous learning simulations after loading the pretrained model, demonstrating its capability to adapt and optimize control parameters dynamically.