An ANN-Based Output-Error-Driven Incremental Model Predictive Control for Buck Converter Against Parameter Variations

DC/DC buck converters have been widely applied in the power processing of distributed energy resources (DERs) penetrated dc microgrid. Model predictive control (MPC) provides optimal control for the buck converter, but it often requires multiple sensors or complex observers to realize zero-steady-state-error control against parameter variations. An output-error-driven incremental MPC (OEDIMPC) for buck converter is proposed in this article. Different from the traditional concept of calculating the optimal duty cycle, the proposed MPC calculates the optimal rate of change of the duty cycle by using a reduced-state output-error-driven prediction model. As a result, the OEDIMPC achieves zero-steady-state-error control against parameter variations, including load value, input voltage, output inductor, and output capacitor, with significantly improved dynamic performance and minimal sensor and observer requirements. Besides, small- and large-signal analyses are used to study the stability boundary of the OEDIMPC under parameter variations, and a shallow layer artificial neural network (ANN) is utilized to realize the OEDIMPC on the hardware. The proposed technique has been evaluated experimentally on a buck converter with resistor load and constant power load (CPL), confirming its effectiveness.