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

Yulin Liu; Tianhao Qie; Yang Yu; Yuxuan Wang; Tat Kei Chau; Xinan Zhang; Ujjal Manandhar; Herbert Iu; Tyrone Fernando

doi:10.1109/TSG.2022.3206281

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

Yulin Liu, Tianhao Qie, Yang Yu, Yuxuan Wang, Tat Kei Chau, Xinan Zhang, Ujjal Manandhar, Herbert Iu, Tyrone Fernando

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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 language	English
Article number	3
Pages (from-to)	1668-1681
Number of pages	14
Journal	IEEE Transactions on Smart Grid
Volume	14
Issue number	3
Early online date	14 Sept 2022
DOIs	https://doi.org/10.1109/TSG.2022.3206281
Publication status	Published - 1 May 2023

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10.1109/TSG.2022.3206281

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

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.",

keywords = "Adaptation models, Batteries, DC Microgrid, Fuel cells, Heuristic algorithms, H∞, Integral Reinforcement Learning, Mathematical models, Microgrids, Observers, Proton Exchange Membrane Fuel Cell",

author = "Yulin Liu and Tianhao Qie and Yang Yu and Yuxuan Wang and Chau, {Tat Kei} and Xinan Zhang and Ujjal Manandhar and Herbert Iu and Tyrone Fernando",

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AU - Liu, Yulin

AU - Qie, Tianhao

AU - Yu, Yang

AU - Wang, Yuxuan

AU - Chau, Tat Kei

AU - Zhang, Xinan

AU - Manandhar, Ujjal

AU - Iu, Herbert

AU - Fernando, Tyrone

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2023/5/1

Y1 - 2023/5/1

N2 - 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.

AB - 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.

KW - Adaptation models

KW - Batteries

KW - DC Microgrid

KW - Fuel cells

KW - Heuristic algorithms

KW - H∞

KW - Integral Reinforcement Learning

KW - Mathematical models

KW - Microgrids

KW - Observers

KW - Proton Exchange Membrane Fuel Cell

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

Abstract

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Advanced model insensitive control methods for energy storage systems in microgrids

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