Iterative Learning Control
Iterative Learning Control is a methodology which aims to increase the performance of machines executing tasks repeatedly. A typical application is in robotic manufacturing when a dedicated robot may complete the same task many thousands of times a day. Iterative Learning Control is also of much interest in the chemical process industry. The key feature, which distinguishes ILC from the allied fields of periodic or repetitive control, concerns the resetting of the initial state. In the generic ILC problem, the internal state of the plant is reset at the start of each task. In repetitive control, the internal state of the plant is at the start of a new task is assumed to be equal to the internal state at the end of the preceeding task.
Iterative Learning Control is a particular example of a repetitive process as studied by Rogers and Owens, and is also closely related to adaptive control. We are particularly interested in robustness and convergence rates for ILC, which have been studied for both predictive and adaptive ILC designs.
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