Bias-Policy Iteration Based Optimal Output Regulation for Partially Unknown Linear Periodic Systems

Huaiyuan Jiang; Xiang Li; Bin Zhou

doi:10.1016/j.ifacol.2025.11.407

Bias-Policy Iteration Based Optimal Output Regulation for Partially Unknown Linear Periodic Systems

Huaiyuan Jiang^*
, Xiang Li
, Bin Zhou

^*Corresponding author for this work

Harbin Institute of Technology
National Key Laboratory of Complex System Control and Intelligent Agent Cooperation

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, the method for the output regulation for a class of partially unknown linear periodic systems is studied. The model-based method for this problem is proposed firstly, where the bias-policy iteration method is utilized in designing the optimal feedback gain. The data-driven algorithm is deduced accordingly, which can approximate the optimal output regulation controller when the controlled system is partially unknown. The effectiveness of the proposed algorithm is verified by the numerical simulation.

Original language	English
Pages (from-to)	1724-1729
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	59
Issue number	20
DOIs	https://doi.org/10.1016/j.ifacol.2025.11.407
State	Published - 1 Aug 2025
Externally published	Yes
Event	23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 - Harbin, China Duration: 2 Aug 2025 → 6 Aug 2025

Keywords

Adaptive dynamic programming
Data-driven control
Linear periodic systems
Optimal control
Output regulation

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10.1016/j.ifacol.2025.11.407

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title = "Bias-Policy Iteration Based Optimal Output Regulation for Partially Unknown Linear Periodic Systems",

abstract = "In this paper, the method for the output regulation for a class of partially unknown linear periodic systems is studied. The model-based method for this problem is proposed firstly, where the bias-policy iteration method is utilized in designing the optimal feedback gain. The data-driven algorithm is deduced accordingly, which can approximate the optimal output regulation controller when the controlled system is partially unknown. The effectiveness of the proposed algorithm is verified by the numerical simulation.",

keywords = "Adaptive dynamic programming, Data-driven control, Linear periodic systems, Optimal control, Output regulation",

author = "Huaiyuan Jiang and Xiang Li and Bin Zhou",

note = "Publisher Copyright: Copyright {\textcopyright} 2025 The Authors.; 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025 ; Conference date: 02-08-2025 Through 06-08-2025",

year = "2025",

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AU - Zhou, Bin

PY - 2025/8/1

Y1 - 2025/8/1

N2 - In this paper, the method for the output regulation for a class of partially unknown linear periodic systems is studied. The model-based method for this problem is proposed firstly, where the bias-policy iteration method is utilized in designing the optimal feedback gain. The data-driven algorithm is deduced accordingly, which can approximate the optimal output regulation controller when the controlled system is partially unknown. The effectiveness of the proposed algorithm is verified by the numerical simulation.

AB - In this paper, the method for the output regulation for a class of partially unknown linear periodic systems is studied. The model-based method for this problem is proposed firstly, where the bias-policy iteration method is utilized in designing the optimal feedback gain. The data-driven algorithm is deduced accordingly, which can approximate the optimal output regulation controller when the controlled system is partially unknown. The effectiveness of the proposed algorithm is verified by the numerical simulation.

KW - Adaptive dynamic programming

KW - Data-driven control

KW - Linear periodic systems

KW - Optimal control

KW - Output regulation

UR - https://www.scopus.com/pages/publications/105025891083

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DO - 10.1016/j.ifacol.2025.11.407

M3 - 会议文章

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SN - 2405-8971

VL - 59

SP - 1724

EP - 1729

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 20

T2 - 23th IFAC Symposium on Automatic Control in Aerospace, ACA 2025

Y2 - 2 August 2025 through 6 August 2025

ER -

Bias-Policy Iteration Based Optimal Output Regulation for Partially Unknown Linear Periodic Systems

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