Toward human-in-the-loop PID control based on CACLA reinforcement learning

Junpei Zhong; Yanan Li

doi:10.1007/978-3-030-27535-8_54

Toward human-in-the-loop PID control based on CACLA reinforcement learning

Junpei Zhong (Corresponding Author), Yanan Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

A self-tuning PID control strategy using a reinforcement learning method, called CACLA (Continuous Actor-critic Learning Automata) is proposed in this paper with the example application of human-in-the-loop physical assistive control. An advantage of using reinforcement learning is that it can be done in an online manner. Moreover, since human is a time-variant system. The demonstration also shows that the reinforcement learning framework would be beneficial to give semi-supervision signal to reinforce the positive learning performance in any time-step.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
Editors	Haibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu, Zhaojie Ju, Dalin Zhou
Publisher	Springer Verlag
Pages	605-613
Number of pages	9
ISBN (Print)	9783030275341
DOIs	https://doi.org/10.1007/978-3-030-27535-8_54
Publication status	Published - 2 Aug 2019
Externally published	Yes
Event	12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 - Shenyang, China Duration: 8 Aug 2019 → 11 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11742 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
Country/Territory	China
City	Shenyang
Period	8/08/19 → 11/08/19

Keywords

Adaptive control
Human-in-the-loop
Reinforcement learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-030-27535-8_54

Cite this

Zhong, J., & Li, Y. (2019). Toward human-in-the-loop PID control based on CACLA reinforcement learning. In H. Yu, J. Liu, L. Liu, Y. Liu, Z. Ju, & D. Zhou (Eds.), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings (pp. 605-613). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11742 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-27535-8_54

Zhong, Junpei ; Li, Yanan. / Toward human-in-the-loop PID control based on CACLA reinforcement learning. Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. editor / Haibin Yu ; Jinguo Liu ; Lianqing Liu ; Yuwang Liu ; Zhaojie Ju ; Dalin Zhou. Springer Verlag, 2019. pp. 605-613 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A self-tuning PID control strategy using a reinforcement learning method, called CACLA (Continuous Actor-critic Learning Automata) is proposed in this paper with the example application of human-in-the-loop physical assistive control. An advantage of using reinforcement learning is that it can be done in an online manner. Moreover, since human is a time-variant system. The demonstration also shows that the reinforcement learning framework would be beneficial to give semi-supervision signal to reinforce the positive learning performance in any time-step.",

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year = "2019",

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Zhong, J & Li, Y 2019, Toward human-in-the-loop PID control based on CACLA reinforcement learning. in H Yu, J Liu, L Liu, Y Liu, Z Ju & D Zhou (eds), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11742 LNAI, Springer Verlag, pp. 605-613, 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019, Shenyang, China, 8/08/19. https://doi.org/10.1007/978-3-030-27535-8_54

Toward human-in-the-loop PID control based on CACLA reinforcement learning. / Zhong, Junpei (Corresponding Author); Li, Yanan.
Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. ed. / Haibin Yu; Jinguo Liu; Lianqing Liu; Yuwang Liu; Zhaojie Ju; Dalin Zhou. Springer Verlag, 2019. p. 605-613 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11742 LNAI).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - A self-tuning PID control strategy using a reinforcement learning method, called CACLA (Continuous Actor-critic Learning Automata) is proposed in this paper with the example application of human-in-the-loop physical assistive control. An advantage of using reinforcement learning is that it can be done in an online manner. Moreover, since human is a time-variant system. The demonstration also shows that the reinforcement learning framework would be beneficial to give semi-supervision signal to reinforce the positive learning performance in any time-step.

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Zhong J, Li Y. Toward human-in-the-loop PID control based on CACLA reinforcement learning. In Yu H, Liu J, Liu L, Liu Y, Ju Z, Zhou D, editors, Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. Springer Verlag. 2019. p. 605-613. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-27535-8_54

Toward human-in-the-loop PID control based on CACLA reinforcement learning

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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