TY - GEN
T1 - Optimization-based constrained iterative learning control with application to building temperature control systems
AU - Peng, Cheng
AU - Sun, Liting
AU - Zhang, Wenlong
AU - Tomizuka, Masayoshi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/26
Y1 - 2016/9/26
N2 - In this paper, an optimization-based constrained iterative learning control (ILC) with an iteratively tunable feedback controller is proposed for building temperature control systems. To guarantee good control performance in the presence of both repetitive and non-repetitive disturbances, the ILC input and the feedback controller are optimized simultaneously in each iteration. Considering constraints from the input saturation, the ILC convergence requirement and the closed-loop stability, the controller design is formulated as a convex optimization problem. The influence of disturbance uncertainties is also incorporated into the optimization problem in the form of chance constraints. To reduce the complexity of the problem, special techniques such as relaxation and projection on convex sets are introduced to make the algorithm more efficient. The effectiveness of the proposed algorithm is verified by simulations conducted on a four-room testbed system.
AB - In this paper, an optimization-based constrained iterative learning control (ILC) with an iteratively tunable feedback controller is proposed for building temperature control systems. To guarantee good control performance in the presence of both repetitive and non-repetitive disturbances, the ILC input and the feedback controller are optimized simultaneously in each iteration. Considering constraints from the input saturation, the ILC convergence requirement and the closed-loop stability, the controller design is formulated as a convex optimization problem. The influence of disturbance uncertainties is also incorporated into the optimization problem in the form of chance constraints. To reduce the complexity of the problem, special techniques such as relaxation and projection on convex sets are introduced to make the algorithm more efficient. The effectiveness of the proposed algorithm is verified by simulations conducted on a four-room testbed system.
UR - http://www.scopus.com/inward/record.url?scp=84992359597&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992359597&partnerID=8YFLogxK
U2 - 10.1109/AIM.2016.7576851
DO - 10.1109/AIM.2016.7576851
M3 - Conference contribution
AN - SCOPUS:84992359597
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 709
EP - 715
BT - 2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2016
Y2 - 12 July 2016 through 15 July 2016
ER -