Abstract
The geometry of successive projection is shown to create opportunities for improving algorithm convergence rates. The two approaches described are those of extrapolation and the introduction of “notches” in the spectrum of the operator describing error evolution. By varying the position of the notch, the resultant algorithm uses NOILC to create a close approximation to the inverse model algorithm.
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© 2016 Springer-Verlag London
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Owens, D.H. (2016). Acceleration and Successive Projection. In: Iterative Learning Control. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-6772-3_13
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DOI: https://doi.org/10.1007/978-1-4471-6772-3_13
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Publisher Name: Springer, London
Print ISBN: 978-1-4471-6770-9
Online ISBN: 978-1-4471-6772-3
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