医用X線発生用トランス共振形位相シフトPWM DC-DCコンバータとファジィ学習制御

抄録

In general, medical-use high-voltage X ray power generator works under a variety of operating conditions of the required output DC voltage and the output current setting values for the X ray tube. A phase-shifted PWM inverter-fed DC-DC power converter with a high-voltage transformer parasitic resonant link which is used for an X ray power generator inherently includes its stiff nonlinear characteristics due to phase-shifted voltage regulation and diode cut off operation in a high-voltage rectifier because of the wide load setting regulation ranges in practical applications. However, superior output voltage response performances of a quick rising time in a transient state and low ripple factor in a steady-state which have to be essentially required for a medical-use X ray power generator could not be sufficiently implemented by various linear modern control approaches because a precise mathematical description with dynamic modeling could not be formulated for wide load variations. For controlling such a nonlinear DC-DC power conversion system, a fuzzy-reasoning-based learning control technique to realize the human capability and much experience to understand the system dynamic and static behavior is more suitable and effective. This paper presents a phase-shifted PWM full bridge inverter type high-voltage DC-DC power converter with a high-voltage transformer parasitic resonant link and its fuzzy-based learning controller, which has excellent dynamic and static behaviors to be utilized in a medical-use X ray power generator operating under conditions of a variety of wide load settings. The effectiveness of feasible fuzzy learning control technique of the DC-DC power converter is discussed and evaluated on the basis of the computer simulation and experimental results in a laboratory set-up. It is proved that the feasible fuzzy-based learning control scheme suitable for this converter is more effective and practical to improve the system output voltage response performances in both the transient and steady states for medical-use X ray power generator. The simulated and experimental results of this converter are actually illustrated in order to substantiate the operating performance validity of the inverter type medical-use X ray power generator operated by the feasible fuzzy-based learning control implementation.