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Iterative Learning Control

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Iterative Learning Control, ILC, is a learning control method concerned with transient performance of systems that operate in a repetitive manner, whether it be a robot arm manipulator, a chemical batch process or the testing of product reliability. ILC takes advantage of these systems' repetitive nature, using information from their past to improve transient performance in the future, an obvious and effective concept traditional control schemes fail to utilise.

T J Harte

Reply to
T Harte
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So how is this different from plain ol' adaptive control with modifications restricted to the feedforward path?

For that matter, since it's gotta be adaptive in some manner, how can you claim that "perfect tracking is achieved with no compromise on the robustness and stability traits of an existing process" -- what guarantees the stability and robustness of your outer (adaptive) loop?

Reply to
Tim Wescott

The internal model principle does. ILC is basically a feedback controller, applicable only to repetitive processes. (e.g. a robot arm in an assembly line). Given the correct compensator structure, the IMP guarantees asymptotic convergence towards zero error, in this case tracking error.

ILC feeds back information form previous trials to the next one. The key idea is this: Assuming that operating conditions, control objective and plant are unchanged between trials, it is possible with knowledge of the controlled plant to correct the feedforward signal to compensate for errors made in the previous trial. These errors would have been the same in the next trial, only they are compensated for.

G.J.

Reply to
Goose

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