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

Tianhao Qie, Xinan Zhang, Jianguo Zhu, Mahinda Vilathgamuwa, Herbert Ho Ching Iu, Tyrone Fernando

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)5400-5410
Number of pages11
JournalIEEE Transactions on Power Electronics
Issue number5
Early online date16 Feb 2024
Publication statusPublished - 1 May 2024


Dive into the research topics of 'A Novel Data-Driven Linear Quadratic Regulator for Interleaved DC/DC Boost Converter'. Together they form a unique fingerprint.

Cite this