AI Control of Power Converters Under Semiconductor Constraints: A Critical Review of Deployment Readiness

Wide-bandgap (WBG) power converters impose stringent requirements, including high-frequency switching, strongly nonlinear dynamics, and limited computational time, which constrain conventional control and artificial intelligence (AI)-based approaches and hinder their practical deployment. Existing review studies have primarily focused on algorithmic structures or performance, while systematic analyses from a deployment feasibility perspective under hardware constraints remain limited. This paper examines AI applications in WBG power converters from a system-level deployment perspective and analyzes existing studies based on implementation feasibility. After outlining the physical characteristics and control requirements of WBG devices, it reviews AI-based modeling, AI-assisted model predictive control (MPC), and reinforcement learning (RL)-based direct control. These approaches are evaluated in terms of computational complexity, real-time feasibility, out-of-distribution (OOD) generalization, and integration with conventional control frameworks. Key deployment challenges, including safety-constrained RL, sim-to-real transfer, and field-programmable gate array (FPGA)/embedded implementation, are treated as core analytical dimensions. To support this assessment, this review introduces an AI Deployment Readiness framework organized around four analytical dimensions: (1) modeling accuracy, (2) safety assurance, (3) sim-to-real transfer capability, and (4) hardware implementability. Using this framework, prior studies are reassessed, and its applicability is further discussed for applications such as fault diagnosis and remaining useful life (RUL) prediction. The analysis identifies key bottlenecks and clarifies deployment-relevant considerations for high-frequency WBG systems.