Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer

Xiao Feng; Hisayoshi Muramatsu; Seiichiro Katsura

doi:10.1109/IECON.2019.8926764

Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer

Xiao Feng, Hisayoshi Muramatsu, Seiichiro Katsura

Department of System Design Engineering

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

2 Citations (Scopus)

Abstract

Periodic disturbances occur during repetitive operation of machines in industrial production. Compensation for the periodic disturbances is an important issue to realize proper machine works beacause the periodic disturbances deteriorate machining precision. In order to eliminate the periodic disturbances, an adaptive periodic-disturbance observer (APDOB) has been proposed as an effective method that can also estimate and compensate for frequency-varying periodic disturbances. However, the APDOB has a problem that design of the APDOB is complicated owing to its six design parameters, which need to be empirically adjusted. Here, we propose an approach based on a genetic algorithm (GA) including a Lévy flight to automatically adjust the six design parameters. The proposed method can remove the conventional empirical design. Moreover, the Lévy flight could improve the exploration ability of the GA by optimizing mutation operator and the best solution found by the GA including Lévy flight could improve the performance of the APDOB.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
Pages	687-692
Number of pages	6
ISBN (Electronic)	9781728148786
DOIs	https://doi.org/10.1109/IECON.2019.8926764
Publication status	Published - 2019 Oct
Event	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal Duration: 2019 Oct 14 → 2019 Oct 17

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2019-October

Conference

Conference	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/Territory	Portugal
City	Lisbon
Period	19/10/14 → 19/10/17

Keywords

Genetic algorithm
Lévy flight
adaptive periodic-disturbance observer
parameter adaptation

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IECON.2019.8926764

Cite this

Feng, X., Muramatsu, H., & Katsura, S. (2019). Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 687-692). Article 8926764 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IECON.2019.8926764

Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer. / Feng, Xiao; Muramatsu, Hisayoshi; Katsura, Seiichiro.
Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2019. p. 687-692 8926764 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October).

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

Feng, X, Muramatsu, H & Katsura, S 2019, Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer. in Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society., 8926764, IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, IEEE Computer Society, pp. 687-692, 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019, Lisbon, Portugal, 19/10/14. https://doi.org/10.1109/IECON.2019.8926764

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abstract = "Periodic disturbances occur during repetitive operation of machines in industrial production. Compensation for the periodic disturbances is an important issue to realize proper machine works beacause the periodic disturbances deteriorate machining precision. In order to eliminate the periodic disturbances, an adaptive periodic-disturbance observer (APDOB) has been proposed as an effective method that can also estimate and compensate for frequency-varying periodic disturbances. However, the APDOB has a problem that design of the APDOB is complicated owing to its six design parameters, which need to be empirically adjusted. Here, we propose an approach based on a genetic algorithm (GA) including a L{\'e}vy flight to automatically adjust the six design parameters. The proposed method can remove the conventional empirical design. Moreover, the L{\'e}vy flight could improve the exploration ability of the GA by optimizing mutation operator and the best solution found by the GA including L{\'e}vy flight could improve the performance of the APDOB.",

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N2 - Periodic disturbances occur during repetitive operation of machines in industrial production. Compensation for the periodic disturbances is an important issue to realize proper machine works beacause the periodic disturbances deteriorate machining precision. In order to eliminate the periodic disturbances, an adaptive periodic-disturbance observer (APDOB) has been proposed as an effective method that can also estimate and compensate for frequency-varying periodic disturbances. However, the APDOB has a problem that design of the APDOB is complicated owing to its six design parameters, which need to be empirically adjusted. Here, we propose an approach based on a genetic algorithm (GA) including a Lévy flight to automatically adjust the six design parameters. The proposed method can remove the conventional empirical design. Moreover, the Lévy flight could improve the exploration ability of the GA by optimizing mutation operator and the best solution found by the GA including Lévy flight could improve the performance of the APDOB.

AB - Periodic disturbances occur during repetitive operation of machines in industrial production. Compensation for the periodic disturbances is an important issue to realize proper machine works beacause the periodic disturbances deteriorate machining precision. In order to eliminate the periodic disturbances, an adaptive periodic-disturbance observer (APDOB) has been proposed as an effective method that can also estimate and compensate for frequency-varying periodic disturbances. However, the APDOB has a problem that design of the APDOB is complicated owing to its six design parameters, which need to be empirically adjusted. Here, we propose an approach based on a genetic algorithm (GA) including a Lévy flight to automatically adjust the six design parameters. The proposed method can remove the conventional empirical design. Moreover, the Lévy flight could improve the exploration ability of the GA by optimizing mutation operator and the best solution found by the GA including Lévy flight could improve the performance of the APDOB.

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Parameter Adjustment Based on Genetic Algorithm for Adaptive Periodic-Disturbance Observer

Abstract

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Keywords

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