TY - JOUR
T1 - Minimum Variance Run-to-Run Controller for General Stochastic Time-Series-Based Disturbances
AU - Lee, An Chen
AU - Huang, Ruei Yu
AU - Chen, Yu Xian
AU - Tsai, Te Hsiu
AU - Chang, Chen Yu
N1 - Publisher Copyright:
© 2020, Chinese Mechanical Engineering Society. All right reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - Exponentially weighted moving average (EWMA) or double EWMA controllers are often employed to deal with various stochastic time-series disturbances for run-to-run control. If the disturbance model is known, then the best EWMA or double EWMA controller can be obtained by minimizing output variance with respect to controller parameters. However, from the theoretical view point, results are only sub-optimum because the control scheme may not be the best control scheme for the underlying stochastic disturbance. Therefore, investigating the best control scheme for the process disturbance following the general ARIMA time series process is worthwhile. In this paper, the predictive disturbance observer (PDOB) is developed based on minimum variance control for various ARIMA(p,r,q) stochastic disturbances. If the ARIMA(p,r,q) disturbance model is known, then the PDOB scheme generates one-step ahead prediction for this disturbance and then feeds it back to compensate the effect of stochastic disturbance on the system output; as a result, the system generates the minimum output variance or simply white noise variance.
AB - Exponentially weighted moving average (EWMA) or double EWMA controllers are often employed to deal with various stochastic time-series disturbances for run-to-run control. If the disturbance model is known, then the best EWMA or double EWMA controller can be obtained by minimizing output variance with respect to controller parameters. However, from the theoretical view point, results are only sub-optimum because the control scheme may not be the best control scheme for the underlying stochastic disturbance. Therefore, investigating the best control scheme for the process disturbance following the general ARIMA time series process is worthwhile. In this paper, the predictive disturbance observer (PDOB) is developed based on minimum variance control for various ARIMA(p,r,q) stochastic disturbances. If the ARIMA(p,r,q) disturbance model is known, then the PDOB scheme generates one-step ahead prediction for this disturbance and then feeds it back to compensate the effect of stochastic disturbance on the system output; as a result, the system generates the minimum output variance or simply white noise variance.
KW - Disturbance observer
KW - Minimum variance control
KW - Run-to-run control
KW - Stochastic disturbance
KW - Time-series model
UR - http://www.scopus.com/inward/record.url?scp=85095854827&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85095854827
SN - 0257-9731
VL - 41
SP - 391
EP - 400
JO - Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao
JF - Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao
IS - 4
ER -