Iterative learning controller for flyback inverter: A hybrid learning scheme

We propose an iterative learning controller (ILC) with hybrid learning scheme for a flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter has advantages such as buck-boost capability, small number of circuit components and high power conversion efficiency, making it suitable for the distributed renewable energy systems. But the conventional proportional-integral (PI) controller for the flyback CCM inverter exhibits poor steady-state response because it suffers from control problems caused by right-half-plane (RHP) zero in closed-loop transfer function and time-varying grid-voltage disturbances. Phase-lead ILC is one of the candidates to solve these problems, but it requires massive amounts of memory. To overcome this problem, we use a sampled-data iterative learning controller with phase-lead compensation, in which sampled-data technique reduces the memory space needed. The proposed ILC is also computationally simple and easy to implement. The stability of the closed-loop system is derived and the zero tracking error is achieved. Experimental tests demonstrate the proposed control approach.