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
AN - SCOPUS:84880124715
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
T2 - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013
Y2 - 22 February 2013 through 23 February 2013
ER -