SUMMARYIn general, medical-use high-voltage X-ray power generators are operated at a variety of required output DC voltages and output current settings for the X-ray tube. A phase-shifted PWM inverter-fed DCDC power converter with a high-voltage transformer parasitic resonant link which is used for an X-ray power generator inherently has stiff nonlinear characteristics due to phase-shifted voltage regulation and diode cutoff operation in a high-voltage rectifier because of the wide load setting ranges in practical applications. However, the superior output voltage response required for a medical-use X-ray power generator could not be sufficiently implemented by various modern linear control approaches because a precise mathematical description with dynamic modeling could not be formulated for wide load variations. For controlling such a nonlinear DCDC power conversion system, a fuzzy reasoning-based learning control technique that duplicates human capabilities and much experience in understanding the dynamic and static behavior of the system are more suitable and effective. This paper presents a phase-shifted PWM full bridge inverter-type high-voltage DCDC power converter with a high-voltage transformer parasitic link and its fuzzy-based learning controller, which has excellent dynamic and static behavior, for use in a medical X-ray power generator operating at a wide variety of load settings. The effectiveness of the feasible fuzzy learning technique used for control of the DCDC power converter is discussed and evaluated based on computer simulations and experimental results in a laboratory setup. It is demonstrated that the feasible fuzzy learning control scheme suitable for this converter is more effective and practical in improving the system output voltage response performance in both transient and steady states for medical-use X-ray power generators. The results of simulations and experiments for this converter are presented in order to illustrate the operating performance of the proposed X-ray power