Iterative learning control

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Iterative Learning Control (ILC) is a method of 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 testing rigs. In each of these tasks the system is required to perform the same action over and over again with high precision.

By using information from previous repetitions, a suitable control action can found iteratively. The internal model principle yields conditions under which perfect tracking can be achieved. A typical control law is of the form:

u p + 1 = u p + K * e p

where $u p$ is the input to the system during the pth repetition, $e p$ is the tracking error during the pth repetition and K is a design parameter.

[edit] References

S.Arimoto, S. Kawamura and F. Miyazaki (1984). "Bettering operation of robots by learning". Journal of Robotic Systems 1: 123–140. doi:10.1002/rob.4620010203.
Moore, K.L. (1993). Iterative Learning Control for Deterministic Systems. London: Springer-Verlag.
Jian-Xin Xu; Ying Tan. (2003). Linear and Nonlinear Iterative Learning Control. Springer-Verlag, 177.
Bristow, D. A. Tharayil, M. Alleyne, A. G. (2006). "A Survey of Iterative Learning Control A learning-based method for high-performance tracking control". IEEE control systems magazine Vol. 26: pages 96–114.
Owens D.H.; Feng K. (20 July 2003). "Parameter optimization in iterative learning control". International Journal of Control Volume 76: 1059–1069. doi:10.1080/0020717031000121410.
Owens D.H. ; Hätönen J. (2005). "Iterative learning control — An optimization paradigm". Annual Reviews in Control 29: 57–70. doi:10.1016/j.arcontrol.2005.01.003.