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

School of Automation Science and Electrical Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	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
Number of pages	7
ISBN (Electronic)	9798350321050
DOIs	https://doi.org/10.1109/DDCLS58216.2023.10166298
State	Published - 2023
Event	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 - Xiangtan, China Duration: 12 May 2023 → 14 May 2023

Publication series

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

Conference

Conference	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Country/Territory	China
City	Xiangtan
Period	12/05/23 → 14/05/23

Keywords

Additive State Decomposition
Non-Affine Systems
Non-minimum Phase Systems
Reinforcement Learning

Access to Document

10.1109/DDCLS58216.2023.10166298

Cite this

Chen, L., & Quan, Q. (2023). Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. In Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023 (pp. 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. pp. 2043-2049 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

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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",

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Chen, L & Quan, Q 2023, Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. in 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., pp. 2043-2049, 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023, Xiangtan, China, 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. p. 2043-2049 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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PB - Institute of Electrical and Electronics Engineers Inc.

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Chen L, Quan Q. Reinforcement Learning for Non-Affine Nonlinear Non-Minimum Phase System Tracking under Additive-State-Decomposition-Based Control Framework. In Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 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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