On line model uncertainty quantification: Hard upper bounds and convergence (original) (raw)

Lecture Notes in Control and Information Sciences

Abstract

This paper considers the problem of on line uncertainty bound quantification in identification of restricted complexity models. Algorithms are presented, which provide hard and tight upper bound on the unknown model uncertainty in H2, H∞ and pointwise sense respectively. The algorithms proposed are very simple, on line and recursive. This allows robust control and adaptive identification to be combined.

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