Analysis and design of iterative learning control strategies for UPS inverters

In this paper, two iterative-learning-based control methods for uninterruptible power system (UPS) inverters, viz., direct iterative learning controller (direct ILC) and hybrid iterative learning controller (hybrid ILC), are proposed. In both methods, a zero-phase filter that is designed in the frequency domain is applied in order to provide compensation for the resonant peak in the system model to ensure error convergence. Furthermore, a "forgetting factor" is introduced in both control algorithms to increase the robustness of the scheme against measurement noise, initialization error, and/or variation of system dynamics due to any parameter drift. In the direct ILC method, ILC is combined with the feedforward of the reference to improve dynamic performance. This method is shown to be capable of achieving very high steady-state performance although its dynamic response was not very good. In the hybrid ILC method, ILC is combined with a proportional-derivative controller aside from the reference feedforward to improve the dynamic response further. Experimental results show that both proposed controllers can achieve very low total harmonic distortion and fast error convergence under different loads while using only one sensor. The proposed direct ILC is an effective solution for UPS products where high-quality steady-state output voltage is more important than fast dynamic response, while hybrid ILC can result in excellent steady-state performance with a more improved dynamic response than direct ILC.