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We propose a downsampled iterative learning controller (ILC) for a flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features step-up/-down ability, small number of circuit components, and high conversion efficiency, so it is well suited to the distributed renewable energy systems. But the conventional proportional-integral (PI) controller for the flyback CCM inverter shows poor steady-state response because the inverter itself suffers from time-varying grid-voltage disturbances and its transfer function has a right-half plane zero. The ILC itself achieves acceptable steady-state response of the flyback CCM inverter but can excite large overshoot in the iteration domain; this overshoot may destroy the solid state devices in the power circuit. To overcome this problem, we use a downsampled ILC technique that guarantees monotonic convergence of tracking error. The proposed ILC is computationally simple and easy to implement. Numerical simulations and experimental tests validate the proposed control approach. [ABSTRACT FROM PUBLISHER]

This paper proposes an iterative learning control (ILC) scheme for a dual-mode flyback inverter operating in both discontinuous conduction mode (DCM) and continuous conduction mode (CCM). In dealing with more than 200-W power applications, the dual-mode flyback inverter takes advantage of both DCM and CCM operations. However, it is difficult to control the output current because the system gain is quite low in DCM and the system transfer function has a right-half plane zero in CCM. To overcome the problems occurring in DCM and CCM operations, we propose an ILC scheme with multiple phase-lead compensation. The ILC is proposed to achieve accurate reference tracking and to reject periodic disturbances. The multiple phase-lead compensation technique is then employed to compensate for the effect of different system dynamics. As a theoretical result, we derive the asymptotic stability of the closed-loop system. We also performed the numerical simulations and experimental tests to validate the proposed control approach. [ABSTRACT FROM AUTHOR]