A Novel Data-Driven Linear Quadratic Regulator for an Interleaved DC/DC Boost Converter

This article proposes a novel data-driven linear quadratic regulator for an interleaved boost dc/dc converter. The proposed method utilizes policy iteration and a simple fixed weight recurrent neuron network to simultaneously achieve model independent control and autonomous online optimal control gain update. Compared to the existing model-based control approaches, the proposed method is totally model free. In addition, the proposed method updates the neural network without any offline pretraining, which is a key advantage for industrial applications. The experimental results, which are obtained using the Texas Instrument C2000 series digital signal processor, are presented to demonstrate the effectiveness of the proposed method.