Adaptive Iterative Learning Control for Linear Systems With Binary-Valued Observations

This brief presents a novel adaptive iterative learning control (ILC) algorithm for a class of single parameter systems with binary-valued observations. Using the certainty equivalence principle, the adaptive ILC algorithm is designed by employing a projection identification algorithm along the iter...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 29; no. 1; pp. 232 - 237
Main Authors: Bu, Xuhui, Hou, Zhongsheng
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Adaptive control
Adaptive learning
Algorithm design and analysis
Algorithms
binary-valued observation
Chemical sensors
Convergence
Equivalence principle
Information systems
iterative learning control (ILC)
Linear systems
Machine learning
Parameter estimation
parameter identification
Parameters
Sensor systems
time-varying parameters
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This brief presents a novel adaptive iterative learning control (ILC) algorithm for a class of single parameter systems with binary-valued observations. Using the certainty equivalence principle, the adaptive ILC algorithm is designed by employing a projection identification algorithm along the iteration axis. It is shown that, even though the available system information is very limited and the desired trajectory is iteration-varying, the proposed adaptive ILC algorithm can guarantee the convergence of parameter estimation over a finite-time interval along the iterative axis; meanwhile, the tracking error is pointwise convergence asymptotically. Two examples are given to validate the effectiveness of the algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2616885