Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework

Lian Chen; Quan Quan

doi:10.1109/DDCLS58216.2023.10166298

Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework

Lian Chen
, Quan Quan

自动化科学与电气工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

This paper proposes a reinforcement-learning additive-state-decomposition-based tracking controller for a class of non-Affine nonlinear non-minimum phase systems. Because the tracking performance is not satisfied with the model-based additive-state-decomposition tracking control with an approximate ideal internal model, two reinforcement learning schemes are introduced to improve the performance under the proposed additive-state-decomposition-based control framework. One is used to generate control commands, and the other is used to generate tracking reference commands. Finally, numerical simulations show the effectiveness of the proposed controller.

源语言	英语
主期刊名	Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
出版商	Institute of Electrical and Electronics Engineers Inc.
页	2043-2049
页数	7
ISBN（电子版）	9798350321050
DOI	https://doi.org/10.1109/DDCLS58216.2023.10166298
出版状态	已出版 - 2023
活动	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 - Xiangtan, 中国期限: 12 5月 2023 → 14 5月 2023

出版系列

姓名	Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

会议

会议	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
国家/地区	中国
市	Xiangtan
时期	12/05/23 → 14/05/23

访问文件

10.1109/DDCLS58216.2023.10166298

其它文件与链接

链接到 Scopus 的出版物

引用此

Chen, L., & Quan, Q. (2023). Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. 在 Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023 (页码 2043-2049). (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS58216.2023.10166298

Chen, Lian ; Quan, Quan. / Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 2043-2049 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

@inproceedings{04633aaedcff4069b009fdfcddabcb15,

title = "Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework",

abstract = "This paper proposes a reinforcement-learning additive-state-decomposition-based tracking controller for a class of non-Affine nonlinear non-minimum phase systems. Because the tracking performance is not satisfied with the model-based additive-state-decomposition tracking control with an approximate ideal internal model, two reinforcement learning schemes are introduced to improve the performance under the proposed additive-state-decomposition-based control framework. One is used to generate control commands, and the other is used to generate tracking reference commands. Finally, numerical simulations show the effectiveness of the proposed controller.",

keywords = "Additive State Decomposition, Non-Affine Systems, Non-minimum Phase Systems, Reinforcement Learning",

author = "Lian Chen and Quan Quan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 ; Conference date: 12-05-2023 Through 14-05-2023",

year = "2023",

doi = "10.1109/DDCLS58216.2023.10166298",

language = "英语",

series = "Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2043--2049",

booktitle = "Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023",

address = "美国",

}

Chen, L & Quan, Q 2023, Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. 在 Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023, Institute of Electrical and Electronics Engineers Inc., 页码 2043-2049, 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023, Xiangtan, 中国, 12/05/23. https://doi.org/10.1109/DDCLS58216.2023.10166298

Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. / Chen, Lian; Quan, Quan.
Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 2043-2049 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework

AU - Chen, Lian

AU - Quan, Quan

PY - 2023

Y1 - 2023

N2 - This paper proposes a reinforcement-learning additive-state-decomposition-based tracking controller for a class of non-Affine nonlinear non-minimum phase systems. Because the tracking performance is not satisfied with the model-based additive-state-decomposition tracking control with an approximate ideal internal model, two reinforcement learning schemes are introduced to improve the performance under the proposed additive-state-decomposition-based control framework. One is used to generate control commands, and the other is used to generate tracking reference commands. Finally, numerical simulations show the effectiveness of the proposed controller.

AB - This paper proposes a reinforcement-learning additive-state-decomposition-based tracking controller for a class of non-Affine nonlinear non-minimum phase systems. Because the tracking performance is not satisfied with the model-based additive-state-decomposition tracking control with an approximate ideal internal model, two reinforcement learning schemes are introduced to improve the performance under the proposed additive-state-decomposition-based control framework. One is used to generate control commands, and the other is used to generate tracking reference commands. Finally, numerical simulations show the effectiveness of the proposed controller.

KW - Additive State Decomposition

KW - Non-Affine Systems

KW - Non-minimum Phase Systems

KW - Reinforcement Learning

UR - https://www.scopus.com/pages/publications/85165951542

U2 - 10.1109/DDCLS58216.2023.10166298

DO - 10.1109/DDCLS58216.2023.10166298

M3 - 会议稿件

AN - SCOPUS:85165951542

T3 - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

SP - 2043

EP - 2049

BT - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023

Y2 - 12 May 2023 through 14 May 2023

ER -

Chen L, Quan Q. Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. 在 Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. 页码 2043-2049. (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023). doi: 10.1109/DDCLS58216.2023.10166298

Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework

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