Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-01
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed.
To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law.
Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense.
The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration.
In addition, the identical initial condition widely used in conventional ILC design is also mitigated.
An example manifests the validity of the proposed ILC algorithm.
American Psychological Association (APA)
Wei, Yun-Shan& Xu, Qing-Yuan. 2018. Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length. Complexity،Vol. 2018, no. 2018, pp.1-6.
https://search.emarefa.net/detail/BIM-1133365
Modern Language Association (MLA)
Wei, Yun-Shan& Xu, Qing-Yuan. Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length. Complexity No. 2018 (2018), pp.1-6.
https://search.emarefa.net/detail/BIM-1133365
American Medical Association (AMA)
Wei, Yun-Shan& Xu, Qing-Yuan. Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length. Complexity. 2018. Vol. 2018, no. 2018, pp.1-6.
https://search.emarefa.net/detail/BIM-1133365
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133365