Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems

Ying Ting Liao, Jason Sheng-Hon Tsai, Hong Tsai, Tzong Jiy Tsai, Shu-Mei Guo, Leang San Shieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes the decentralized iterative learning control (ILC) for a class of unknown sampled-data interconnected large-scale nonlinear with a closed-loop decoupling property via the off-line observer/Kalman filter identification (OKID) method. First, the OKID method not only is utilized to determine decentralized appropriate (low-) order discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data but also to overcome the effect of modeling error on the identified linear model of each subsystem. For the tracking purpose, a norm-optimal ILC (NOILC) scheme is embedded to the decentralized models, and the constrained ILC problem is formulated in a successive projection framework. To reduce unwanted learning cycles, the digital-redesign linear quadratic tracker with the high-gain property is proposed to assign the initial control input of ILC. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed methodologies.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013
Pages104-110
Number of pages7
DOIs
Publication statusPublished - 2013 Jul 18
Event2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013 - Kerala, India
Duration: 2013 Feb 222013 Feb 23

Publication series

NameProceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013

Other

Other2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013
CountryIndia
CityKerala
Period13-02-2213-02-23

Fingerprint

Nonlinear systems
Kalman filters
Identification (control systems)

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Control and Systems Engineering

Cite this

Liao, Y. T., Tsai, J. S-H., Tsai, H., Tsai, T. J., Guo, S-M., & Shieh, L. S. (2013). Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems. In Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013 (pp. 104-110). [6526391] (Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013). https://doi.org/10.1109/iMac4s.2013.6526391
Liao, Ying Ting ; Tsai, Jason Sheng-Hon ; Tsai, Hong ; Tsai, Tzong Jiy ; Guo, Shu-Mei ; Shieh, Leang San. / Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems. Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. 2013. pp. 104-110 (Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013).
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abstract = "This paper proposes the decentralized iterative learning control (ILC) for a class of unknown sampled-data interconnected large-scale nonlinear with a closed-loop decoupling property via the off-line observer/Kalman filter identification (OKID) method. First, the OKID method not only is utilized to determine decentralized appropriate (low-) order discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data but also to overcome the effect of modeling error on the identified linear model of each subsystem. For the tracking purpose, a norm-optimal ILC (NOILC) scheme is embedded to the decentralized models, and the constrained ILC problem is formulated in a successive projection framework. To reduce unwanted learning cycles, the digital-redesign linear quadratic tracker with the high-gain property is proposed to assign the initial control input of ILC. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed methodologies.",
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Liao, YT, Tsai, JS-H, Tsai, H, Tsai, TJ, Guo, S-M & Shieh, LS 2013, Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems. in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013., 6526391, Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, pp. 104-110, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, India, 13-02-22. https://doi.org/10.1109/iMac4s.2013.6526391

Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems. / Liao, Ying Ting; Tsai, Jason Sheng-Hon; Tsai, Hong; Tsai, Tzong Jiy; Guo, Shu-Mei; Shieh, Leang San.

Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. 2013. p. 104-110 6526391 (Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - This paper proposes the decentralized iterative learning control (ILC) for a class of unknown sampled-data interconnected large-scale nonlinear with a closed-loop decoupling property via the off-line observer/Kalman filter identification (OKID) method. First, the OKID method not only is utilized to determine decentralized appropriate (low-) order discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data but also to overcome the effect of modeling error on the identified linear model of each subsystem. For the tracking purpose, a norm-optimal ILC (NOILC) scheme is embedded to the decentralized models, and the constrained ILC problem is formulated in a successive projection framework. To reduce unwanted learning cycles, the digital-redesign linear quadratic tracker with the high-gain property is proposed to assign the initial control input of ILC. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed methodologies.

AB - This paper proposes the decentralized iterative learning control (ILC) for a class of unknown sampled-data interconnected large-scale nonlinear with a closed-loop decoupling property via the off-line observer/Kalman filter identification (OKID) method. First, the OKID method not only is utilized to determine decentralized appropriate (low-) order discrete-time linear models for the class of unknown interconnected large-scale sampled-data systems by using known input-output sampled data but also to overcome the effect of modeling error on the identified linear model of each subsystem. For the tracking purpose, a norm-optimal ILC (NOILC) scheme is embedded to the decentralized models, and the constrained ILC problem is formulated in a successive projection framework. To reduce unwanted learning cycles, the digital-redesign linear quadratic tracker with the high-gain property is proposed to assign the initial control input of ILC. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed methodologies.

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Liao YT, Tsai JS-H, Tsai H, Tsai TJ, Guo S-M, Shieh LS. Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems. In Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. 2013. p. 104-110. 6526391. (Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013). https://doi.org/10.1109/iMac4s.2013.6526391