Abstract
This paper presents the design of Iterative Learning Control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 |
| Pages | 1805-1810 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - Dec 1 2008 |
| Externally published | Yes |
| Event | 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 - Hanoi, Viet Nam Duration: Dec 17 2008 → Dec 20 2008 |
Publication series
| Name | 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 |
|---|
Other
| Other | 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008 |
|---|---|
| Country | Viet Nam |
| City | Hanoi |
| Period | 12/17/08 → 12/20/08 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
An LMI approach for robust iterative learning control with quadratic performance criterion. / Nguyen, Dinh Hoa; Banjerdpongchai, David.
2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008. 2008. p. 1805-1810 4795802 (2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - An LMI approach for robust iterative learning control with quadratic performance criterion
AU - Nguyen, Dinh Hoa
AU - Banjerdpongchai, David
PY - 2008/12/1
Y1 - 2008/12/1
N2 - This paper presents the design of Iterative Learning Control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.
AB - This paper presents the design of Iterative Learning Control based on Quadratic performance criterion (Q-ILC) for linear systems subject to additive uncertainty. Robust Q-ILC design can be cast as a min-max problem. We propose a novel approach which employs an upper bound of the worst-case error, then formulates a nonconvex quadratic minimization problem to get the update of iterative control inputs. Applying Langrange duality, the Lagrange dual function of the nonconvex quadratic problem is equivalent to a convex optimization over linear matrix inequalities (LMIs). An LMI algorithm with convergence properties is then given for the robust Q-ILC. Finally, we provide a numerical example to illustrate the effectiveness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=64549160146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=64549160146&partnerID=8YFLogxK
U2 - 10.1109/ICARCV.2008.4795802
DO - 10.1109/ICARCV.2008.4795802
M3 - Conference contribution
AN - SCOPUS:64549160146
SN - 9781424422876
T3 - 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
SP - 1805
EP - 1810
BT - 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
ER -