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Iterative learning control (ILC) has been extensively used in systems that repeatedly follow the same desired trajectory. The key idea is to incorporate the tracking errors from previous iterations to generate a better feedforward signal for the next iteration. A drawback of ILC is that all disturbances are assumed to be repetitive, while in practice non-repetitive disturbances may also affect the...
In precision motion systems, the same desired trajectory may have to be repeatedly followed. In such cases, iterative learning control (ILC) is a useful strategy to improve the tracking performance at every iteration cycle. The fundamental assumption is that the error is due to repetitive disturbances. In practice, however, non-repetitive disturbances may also be present, and non-repetitive and repetitive...
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