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Repetition allows the system to sequentially improve tracking accuracy, in effect learning the required input needed to track the reference as closely as possible. The learning process uses information from previous repetitions to improve the control signal, ultimately enabling a suitable control
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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
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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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is crucial to achieving design objectives (i.e. trading off fast convergence and robust performance) and ranges from simple scalar gains to sophisticated optimization computations.
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is a low-pass filtering matrix. This removes high-frequency disturbances which may otherwise be aplified during the learning process.
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Wang Y.; Gao F.; Doyle III, F.J. (2009). "Survey on iterative learning control, repetitive control, and run-to-run control".
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In many cases a low-pass filter is added to the input to improve performance. The control law then takes the form
660:(2006). "A Survey of Iterative Learning Control A learning-based method for high-performance tracking control".
572:(2006). "A Survey of Iterative Learning Control A learning-based method for high-performance tracking control".
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rigs. In each of these tasks the system is required to perform the same action over and over again with high
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for systems that work in a repetitive mode. Examples of systems that operate in a repetitive manner include
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Owens D.H.; Feng K. (20 July 2003). "Parameter optimization in iterative learning control".
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Owens D.H.; Feng K. (20 July 2003). "Parameter optimization in iterative learning control".
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S.Arimoto, S. Kawamura; F. Miyazaki (1984). "Bettering operation of robots by learning".
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S.Arimoto, S. Kawamura; F. Miyazaki (1984). "Bettering operation of robots by learning".
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Owens D.H.; Hätönen J. (2005). "Iterative learning control — An optimization paradigm".
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is the input to the system during the pth repetition,
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Iterative
Learning Control for Deterministic Systems
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Learning Control for Deterministic Systems
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is the tracking error during the pth repetition and
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432:{\displaystyle u_{p+1}=Q(u_{p}+K*e_{p})}
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208:{\displaystyle u_{p+1}=u_{p}+K*e_{p}}
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671:International Journal of Control
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535:International Journal of Control
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712:10.1016/j.arcontrol.2005.01.003
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637:. Springer-Verlag. p.
611:. London: Springer-Verlag.
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512:. London: Springer-Verlag.
134:on a finite time interval.
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751:Journal of Process Control
588:Journal of Robotic Systems
483:Journal of Robotic Systems
87:Iterative Learning Control
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700:Annual Reviews in Control
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58:more precise citations.
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629:; Ying Tan. (2003).
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50:Please help
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627:Jian Xin Xu
140:iteratively
64:August 2011
56:introducing
468:References
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103:precision
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