TY - GEN
T1 - Kalman filter based iterative learning control for discrete time MIMO systems
AU - Jayawardhana, Rangana N.
AU - Ghosh, Bijoy K.
N1 - Funding Information:
Partially supported by the Dick and Martha Professorship at the Mathematics and Statistics Dept., Texas Tech University.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/6
Y1 - 2018/7/6
N2 - A new iterative learning control (ILC) algorithm for linear, time invariant, multi-input multi-output, dynamical systems in discrete time is introduced. The gain of the well known P-type ILC algorithm is updated, in every iteration, by solving a discrete Riccati equation that requires the knowledge of the first Markov parameter matrix CB of the dynamical system. The proposed Kalman Filter based learning algorithm is implementable block-wise which is similar to the, recently introduced, Luenberger observer based ILC algorithm.
AB - A new iterative learning control (ILC) algorithm for linear, time invariant, multi-input multi-output, dynamical systems in discrete time is introduced. The gain of the well known P-type ILC algorithm is updated, in every iteration, by solving a discrete Riccati equation that requires the knowledge of the first Markov parameter matrix CB of the dynamical system. The proposed Kalman Filter based learning algorithm is implementable block-wise which is similar to the, recently introduced, Luenberger observer based ILC algorithm.
KW - Kalman filter
KW - block implementation
KW - iterative learning control
UR - http://www.scopus.com/inward/record.url?scp=85050876214&partnerID=8YFLogxK
U2 - 10.1109/CCDC.2018.8407502
DO - 10.1109/CCDC.2018.8407502
M3 - Conference contribution
AN - SCOPUS:85050876214
T3 - Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
SP - 2257
EP - 2264
BT - Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 June 2018 through 11 June 2018
ER -