A Model-Independent Online Learning-Based Control Strategy for DC/AC Inverters

This letter proposes a novel control scheme for power electronic inverters using a barrier lyapunov function (BLF) guided radial basis function neural network (RBFNN) controller, featuring online learning and real-time applicability. Unlike many existing adaptive neural network based controller, the proposed method requires no knowledge of system parameters and does not require any offline training. The control law is updated entirely online with guaranteed convergence, ensuring bounded current tracking under uncertainties and disturbances. Its simple structure leads to extremely low computational complexity, making it one of the most efficient model-free controller currently applicable to real-time DC-AC inverter control. The effectiveness and robustness of proposed controller are verified through application to a three-level neutral-point-clamped (3L17 NPC) inverter.