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Consider the ILC updating law (11) applied to the repetitive system (1) under (2), (3) and satisfying Assumptions 2.1 and 2.2.
One of those changes involved developing a new artificial intelligence system they have called Mana, which is designed to help customers automate repetitive system maintenance tasks and build knowledge about the underlying systems using artificial intelligence and machine learning.
Overviewing the existing ILC investigations, the ILC has been acknowledged as one of the most effective intelligent control strategies for a repetitive system operated over a fixed time interval owing to its less system information requirement and precise tracking insurance [2 8].
The pioneer contribution is the iterative learning control (ILC) invented in the 1980s whose scheme is to utilize the historical tracking discrepancy to modify its control command so that the upgraded control command may drive the repetitive system to track a predetermined desired trajectory [1].
Adrian Iredale, from architects Iredale Pederson Hook, described the building as "simultaneously elegant and heavy and introducing poetry through the repetitive system of sun shading, the big Ts that hover in front of the facade on all sides".
Although the magnitude of the peptide bond deviations is limited, its significance is increased in a repetitive system as collagen triple helix.
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Past work in this field has dealt mainly with single energy pulses, whereas current work deals more with repetitive systems in which repetition rates often exceed 1000 pulses s− 1.
It also sheds light on the mechanism of localization phenomena for this class of repetitive systems.
Furthermore, the expected length of the plasma discharges will require steady-state repetitive systems.
Iterative learning control (ILC) is a method for improving the performance of stable, repetitive systems.
A popular feedforward control technique for repetitive systems is iterative learning control (ILC).
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