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

This paper proposes a novel data-driven linear quadratic regulator for 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. Additionally, the proposed method updates the neural network without any offline pre-training, 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.