TY - JOUR
T1 - Differential Evolutionary Algorithm with Local Search for the Adaptive Periodic-Disturbance Observer Adjustment
AU - Feng, Xiao
AU - Muramatsu, Hisayoshi
AU - Katsura, Seiichiro
N1 - Funding Information:
Manuscript received May 8, 2020; revised August 9, 2020 and October 19, 2020; accepted November 9, 2020. Date of publication December 3, 2020; date of current version August 25, 2021. This work was supported by the Ministry of Internal Affairs and Communications, Strategic Information and Communications R&D Promotion Programme (SCOPE), 201603011, 2020. (Corresponding author: Seiichiro Katsura.) Xiao Feng and Seiichiro Katsura are with the Department of System Design Engineering, Keio University, Yokohama 223-8522, Japan (e-mail: fengxiao@katsura.sd.keio.ac.jp; katsura@sd.keio.ac.jp).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which makes the compensation difficult. To eliminate the frequency-varying periodic disturbance, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This article proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Lévy flight. Moreover, the proposed method can reduce the number of the design parameters.
AB - Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which makes the compensation difficult. To eliminate the frequency-varying periodic disturbance, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This article proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Lévy flight. Moreover, the proposed method can reduce the number of the design parameters.
KW - Adaptive periodic-disturbance observer (APDOB)
KW - Lévy flight
KW - differential evolutionary (DE) algorithm
KW - memetic algorithm
KW - periodic disturbance
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U2 - 10.1109/TIE.2020.3040664
DO - 10.1109/TIE.2020.3040664
M3 - Article
AN - SCOPUS:85097931449
VL - 68
SP - 12504
EP - 12512
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 12
M1 - 9280360
ER -