Paper Info
Title
An Iterative Learning Control Synthesis For Nonlinear Systems With Hard Input And Output Constraints
Abstract
Engineered systems are always subjected to operational constraints that limit the range of feasible control input signals and their output signals. This paper proposes an iterative learning control (ILC) structure that can satisfy hard input and output constraints simultaneously for a class of non-linear systems. This structure enables the decoupling between the design of feed-forward ILC and the output feedback. The role of feed-forward ILC is to track the desired trajectory under repetitive environment while the output feedback is added to handle output constraints with the help of a barrier Lyapunov function. The concept of virtual output constraints is proposed to ensure that the output constraints can be satisfied within the input limits by shifting and scaling the original barrier Lyapunov function. The proposed algorithm is able to ensure the perfect tracking performance and satisfaction of both input and output hard constraints. Simulation results are presented to demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2019
10.1109/ICCA.2019.8899986
2019 IEEE 15TH INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA)
Field
DocType
ISSN
Nonlinear system,Control theory,Barrier lyapunov function,Decoupling (cosmology),Input/output,Control engineering,Iterative learning control,Engineering,Scaling,Trajectory
Conference
1948-3449
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Gijo Sebastian192.88
Ying Tan273786.47
Denny Oetomo310031.30