Iterative Learning Control based on Stochastic Approximation

In this paper stochastic approximation theory is used to produce Iterative Learning Control algorithms which are less sensitive to stochastic disturbances, a typical problem for the learning process of standard ILC algorithms. Two algorithms are developed, one to obtain zero mean controlled error and one to minimise the mean 2-norm of the controlled error. The former requires a certain knowledge of the system but in the presence of noise can give reasonably rapid convergence. The latter can either use a model or be model free by employing a second experiment. Simulations have been carried out to demonstrate the effectiveness of the methods.


Presented at:
IFAC World Congress 2008, Seoul, July 6-11, 2008
Year:
2008
Keywords:
Note:
Prj_DDMethodTracking
Laboratories:




 Record created 2007-09-21, last modified 2018-01-28

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