Paper Info
Title
Robust boundary iterative learning control for a class of nonlinear hyperbolic systems with unmatched uncertainties and disturbance.
Abstract
In this paper, the robust boundary iterative learning control for the output tracking and disturbance attenuation of the 2 × 2 nonlinear hyperbolic system is addressed. Since the measurement limitation, the control and measurement are implemented at the same boundary of the system and the disturbance is not necessary to be estimated, which makes the iterative learning control be easy in implementation and low in measurement cost. By using the characteristic method, the robust convergence with respect to iteration-varying uncertainties arising from initial states shift, external disturbances, model plants uncertainties and disturbed reference trajectories is analyzed without any model reduction, rigorously. It is shown that the robust convergence bound is continuously dependent on the bounds of the iteration-varying uncertainties. Furthermore, to implement the proposed iterative learning control, the actuator dynamic is considered, also. Finally, with the actuator dynamic, two examples are given to demonstrate the effectiveness of the proposed iterative learning control strategy for the 2 × 2 nonlinear hyperbolic system.
Year
DOI
Venue
2018
10.1016/j.neucom.2018.09.020
Neurocomputing
Keywords
Field
DocType
Disturbance rejection,Iterative learning control,Hyperbolic system,Characteristic method
Convergence (routing),Nonlinear system,Pattern recognition,Control theory,Hyperbolic systems,Artificial intelligence,Iterative learning control,Attenuation,Mathematics,Actuator
Journal
Volume
ISSN
Citations 
321
0925-2312
0
PageRank 
References 
Authors
0.34
25
2
Name
Order
Citations
PageRank
Chao He171.12
Jun-Min LI239036.09