Two recent developments will be surveyed here which are pointing the way towards an input-output theory of H-infinity-l(1) adaptive feedback: The solution of problems involving; (1) feedback performance (exact) optimization under large plant uncertainty on the one hand (the two-disc problem of H-infinity); and (2) optimally fast identification in H-infinity on the other. Taken together, these are yielding adaptive algorithms for slowly varying data in H-infinity-l(1). At a conceptual level, these results motivate a general input-output theory linking identification, adaptation, and control learning. In such a theory, the definition of adaptation is based on system performance under uncertainty, and is independent of internal structure, presence or absence of variable parameters, or even feedback. (C) 1998 Elsevier Science B.V. All rights reserved. [References: 22]
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