Data-Driven Generalized Minimum Variance Regulatory Control for CARMA Models with Unknown Disturbance Characteristics
Author:
Affiliation:
1. Graduate School of System Design, Tokyo Metropolitan University
Publisher
Institute of Electrical Engineers of Japan (IEE Japan)
Subject
Electrical and Electronic Engineering
Link
https://www.jstage.jst.go.jp/article/ieejeiss/136/7/136_907/_pdf
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3. (3) M. C. Campi, A. Lecchini, and S. M. Savaresi: “Virtual reference feedback tuning: A direct method for the design of feedback controllers”, Automatica, Vol. 38, No. 8, pp. 1337-1346 (2002)
4. (4) S. Soma, O. Kaneko, and T. Fujii: “A new approach to parameter tuning of controllers by using one-shot experimental data—A proposal of fictitious reference iterative tuning”, Trans. of the Institute of Systems, Control, and Information Engineers, Vol. 17, No. 12, pp. 528-536 (2004)
5. (5) K. Tasaka, M. Kano, M. Ogawa, S. Masuda, and T. Yamamoto: “Direct PID tuning from closed-loop data and its application to unstable processes”, Trans. of the Institute of Systems, Control, and Information Engineers, Vol. 22, No. 4, pp. 137-144 (2009)
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1. Data-driven PID Gain Tuning from Regulatory Control Data Based on Generalized Minimum Variance Evaluation;IEEJ Transactions on Electronics, Information and Systems;2017
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