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 language | English |
|---|---|
| Title of host publication | Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013 |
| Pages | 104-110 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2013 Jul 18 |
| Event | 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013 - Kerala, India Duration: 2013 Feb 22 → 2013 Feb 23 |
Publication series
| Name | Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013 |
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Other
| Other | 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013 |
|---|---|
| Country | India |
| City | Kerala |
| Period | 13-02-22 → 13-02-23 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Control and Systems Engineering
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - Effective tracker design based on iterative learning control methodology with input constraint for a class of unknown interconnected large-scale sampled-data nonlinear systems
AU - Liao, Ying Ting
AU - Tsai, Jason Sheng-Hon
AU - Tsai, Hong
AU - Tsai, Tzong Jiy
AU - Guo, Shu-Mei
AU - Shieh, Leang San
PY - 2013/7/18
Y1 - 2013/7/18
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.
UR - http://www.scopus.com/inward/record.url?scp=84880124715&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84880124715&partnerID=8YFLogxK
U2 - 10.1109/iMac4s.2013.6526391
DO - 10.1109/iMac4s.2013.6526391
M3 - Conference contribution
SN - 9781467350891
T3 - Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013
SP - 104
EP - 110
BT - Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013
ER -