Data-Driven Multirate Predictive Control of Power Inverters based on Kinky Inference

Model predictive control is a very appealing methodology to control multi-level power converters, but presents an important drawback in the fact that the control input is held constant during each sampling period. Multirate model predictive control has been proposed to alleviate this issue, but it is significantly more complex to compute in systems operating at high frequencies. In this work, we propose the data-driven implementation of multirate model predictive control using the machine-learning method known as Kinky Inference, in an effort to make this control technique possible to be implemented in the future in specialized hardware. The method is tested in simulation on a five-level diode-clamped converter operating in inverter mode. Results show a significant reduction in THD due to the multirate technique, and Kinky Inference succeeds in learning the control law.