DocumentCode
526566
Title
Notice of Retraction
NP-D-INP-D-type ILC for a class of nonlinear systems
Author
Bai-shun Liu ; Feng-Lei Ding ; Bing-Li Tian
Author_Institution
Dept. of Battle & Command, Academe of Naval Submarine, Qingdao, China
Volume
2
fYear
2010
fDate
9-11 July 2010
Firstpage
346
Lastpage
350
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we presents a new nonlinear iterative learning control law for a class of nonlinear systems, denoted NP-D-INP-D, which is composed by limiting the action of error in PID learning law, that is, use a bounded nonlinear function of error instead of error, and adding a differential feedback in the integrator of PID learning law to inject the suited damping. By using Bellman-Gronwall lemma, the simple explicit conditions on the learning gains to ensure convergence are provided in the sense of Lambda norm. An attractive feature of NP-D-INP-D-type learning law is that it is of faster convergence speed, better flexibility and higher accuracy. The simulation example demonstrates the effectiveness and advantage of NP-D-INP-D-type learning law.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we presents a new nonlinear iterative learning control law for a class of nonlinear systems, denoted NP-D-INP-D, which is composed by limiting the action of error in PID learning law, that is, use a bounded nonlinear function of error instead of error, and adding a differential feedback in the integrator of PID learning law to inject the suited damping. By using Bellman-Gronwall lemma, the simple explicit conditions on the learning gains to ensure convergence are provided in the sense of Lambda norm. An attractive feature of NP-D-INP-D-type learning law is that it is of faster convergence speed, better flexibility and higher accuracy. The simulation example demonstrates the effectiveness and advantage of NP-D-INP-D-type learning law.
Keywords
adaptive control; convergence; error statistics; iterative methods; learning systems; manipulators; nonlinear control systems; nonlinear functions; position control; three-term control; Bellman-Gronwall lemma; NP-D-INP-D-type ILC; PID learning law; bounded nonlinear function; differential feedback; lambda norm; nonlinear iterative learning control law; nonlinear system; Convergence analysis; Iterative learning control; Nonlinear system; Robot control;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564602
Filename
5564602
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