A Novel Integral Reinforcement Learning based H∞ Control Strategy for Proton Exchange Membrane Fuel Cell in DC Microgrids

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, a novel integral reinforcement learning (IRL) based H∞ control algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to enhance its performance in DC microgrids. The proposed control algorithm produces superior dynamic and steady-state responses without requiring the system model information. The stability of the proposed control algorithm is rigorously proven. Furthermore, its superiority over the PI control, model predictive control (MPC), fuzzy PI control and adaptive MPC is analyzed. Hardware-in-the-loop experimental results are given to verify the effectiveness of the proposed control algorithm.

Original languageEnglish
Article number3
Pages (from-to)1668-1681
Number of pages14
JournalIEEE Transactions on Smart Grid
Volume14
Issue number3
Early online date14 Sept 2022
DOIs
Publication statusPublished - 1 May 2023

Fingerprint

Dive into the research topics of 'A Novel Integral Reinforcement Learning based H∞ Control Strategy for Proton Exchange Membrane Fuel Cell in DC Microgrids'. Together they form a unique fingerprint.

Cite this