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| | Hello and welcome. My title is Irwin and I completely dig that title. Managing people has been his day occupation for a while. Playing baseball is the hobby he will by no means quit performing. South Dakota is where I've usually been residing.<br><br>my website: std testing at home - [http://www.zavodpm.ru/blogs/ashelykeplerlkws/9653-valuable-guidance-successfully-treating-candida-albicans visit your url], |
| '''Iterative Learning Control''' (ILC) is a method of [[Process control|tracking control]] for systems that work in a repetitive mode. Examples of systems that operate in a repetitive manner include [[robot]] arm manipulators, chemical batch processes and [[Reliability engineering|reliability testing]] rigs. In each of these tasks the system is required to perform the same action over and over again with high [[Accuracy and precision|precision]]. This action is represented by the objective of accurately tracking a chosen reference signal <math>r(t)</math> on a finite time interval.
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| Repetition allows the system to improve tracking accuracy from repetition to repetition, in effect learning the required input needed to track the reference exactly. The learning process uses information from previous repetitions to improve the control signal ultimately enabling a suitable control action can be found [[iteration|iteratively]]. The [[internal model (motor control)|internal model]] principle yields conditions under which perfect tracking can be achieved but the design of the control algorithm still leaves many decisions to be made to suit the application. A typical, simple control law is of the form:
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| :<math>u_{p+1}=u_p +K * e_p</math>
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| where <math>u_p</math> is the input to the system during the pth repetition, <math>e_p</math> is the tracking error during the pth repetition and K is a design parameter representing operations on <math>e_p</math>. Achieving perfect tracking through iteration is represented by the mathematical requirement of convergence of the input signals as <math>p</math> becomes large whilst the rate of this convergence represents the desirable practical need for the learning process to be rapid. There is also the need to ensure good algorithm performance even in the presence of uncertainty about the details of process dynamics. The operation <math>K</math> is crucial to achieving design objectives and ranges from simple scalar gains to sophisticated optimization computations. | |
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| ==References==
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| *{{Cite journal
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| | author = S.Arimoto, S. Kawamura and F. Miyazaki
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| | title = Bettering operation of robots by learning
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| | journal = Journal of Robotic Systems
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| | year = 1984
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| | volume = 1
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| | issue = 2
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| | pages = 123–140
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| | doi = 10.1002/rob.4620010203
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| }}
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| *{{Cite book
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| | last = Moore
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| | first = K.L.
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| | year = 1993
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| | title = Iterative Learning Control for Deterministic Systems
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| | publisher = Springer-Verlag
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| | location = London
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| | isbn = 0-387-19707-9
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| }}
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| *{{Cite book
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| | author = Jian-Xin Xu; Ying Tan.
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| | year = 2003
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| | title = Linear and Nonlinear Iterative Learning Control
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| | publisher = Springer-Verlag
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| | page = 177
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| | isbn = 3-540-40173-3
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| }}
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| *{{Cite journal
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| | author = Bristow, D. A. Tharayil, M. Alleyne, A. G.
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| | title = A Survey of Iterative Learning Control A learning-based method for high-performance tracking control
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| | journal = IEEE control systems magazine
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| | year = 2006
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| | volume = 26
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| | pages = pages 96–114
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| }}
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| *{{Cite journal
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| | author = Owens D.H.; Feng K.
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| | title = Parameter optimization in iterative learning control
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| | journal = International Journal of Control
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| | date = 20 July 2003
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| | volume = 76
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| | issue = 11
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| | pages = 1059–1069
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| | doi = 10.1080/0020717031000121410
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| }}
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| *{{Cite journal
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| | author = Owens D.H. ; Hätönen J.
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| | title = Iterative learning control — An optimization paradigm
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| | journal = Annual Reviews in Control
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| | year = 2005
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| | volume = 29
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| | issue = 1
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| | pages = 57–70
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| | doi = 10.1016/j.arcontrol.2005.01.003
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| }}
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| *{{Cite journal
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| | author = Daley S. ; Owens D.H.
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| | title = Iterative Learning Control – Monotonicity and Optimization
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| | journal = International Journal of applied mathematics and computer science
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| | year = 2008
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| | volume = 18
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| | issue = 3
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| | pages = 179–293
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| | doi = 10.2478/v10006-008-0026-7
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| }}
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| *{{Cite journal
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| | author = Wang Y. ; Gao F. ; Doyle III, F.J.
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| | title = Survey on iterative learning control, repetitive control, and run-to-run control
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| | journal = Journal of process control
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| | year = 2009
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| | volume = 19
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| | issue = 10
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| | pages = 1589–1600
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| | doi = 10.1016/j.jprocont.2009.09.006
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| }}
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| ==External links==
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| *[http://www.sheffield.ac.uk/ilc Southampton Sheffield Iterative Learning Control (SSILC)]
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| {{Use dmy dates|date=September 2010}}
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| {{DEFAULTSORT:Iterative Learning Control}}
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| [[Category:Control theory]]
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| {{applied-math-stub}}
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Hello and welcome. My title is Irwin and I completely dig that title. Managing people has been his day occupation for a while. Playing baseball is the hobby he will by no means quit performing. South Dakota is where I've usually been residing.
my website: std testing at home - visit your url,