A Robust Iterative Learning Control Technique to Efficiently Mitigate Disturbances for Three-Phase Standalone Inverters

Abstract

This article investigates a robust iterative learning control (ILC) technique that effectively rejects the influence of periodic and nonperiodic disturbances for a three-phase constant-voltage constant-frequency standalone voltage source inverter (VSI) with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> filter under variable initial states. In conventional ILC, the learning dynamics are more complex when the initial iterative state is different at each iteration due to the fixed initial state value. Unlike conventional ILC, the proposed ILC follows a transformed dynamic model for robust learning rule convergence that is less restricted under varying initial states and significantly eliminates the impact of periodic and nonperiodic disturbances. Moreover, a simplified stability analysis is provided, and the conditions required for robust learning rule convergence are discussed. A comparative verification with the results of conventional ILC using a TI TMS320F28335 digital signal processor based prototype standalone VSI proves that the proposed ILC technique offers robust and effective steady-state performance, with benefits such as reduced steady-state errors and low total harmonic distortion under periodic disturbances. Finally, its improved robustness and fast transient-state performance are validated under nonperiodic disturbances due to the existence of tough load conditions, i.e., step-changes of linear, unbalanced, and nonlinear loads with significantly distorted model parameters.