Asymptotic performance in adaptive H control

Sundeep Rangan, Kameshwar Poolla

Research output: Chapter in Book/Report/Conference proceedingChapter


In adaptive control, it is often useful to distinguish between transient and asymptotic performance. In this paper, we formulate a notion of asymptotic performance for an H adaptive control problem, and consider the problem in the simple case where the unknown plant is one of a finite number of known, possible models. We consider two plant cases: a) the plants are simply static nonlinear functions, and b) the plants are linear and time-invariant. Our main result is that, for both cases, the optimal asymptotic H performance is no better than the optimal performance in the transient phase. We conclude that increased input-output data does not improve the achievable H performance. Instead, parametric uncertainty results in a persistent performance degradation, and this uncertainty cannot be resolved, even with infinite data.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Editors Anon
Number of pages5
StatePublished - 1996
EventProceedings of the 35th IEEE Conference on Decision and Control. Part 4 (of 4) - Kobe, Jpn
Duration: Dec 11 1996Dec 13 1996

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216


OtherProceedings of the 35th IEEE Conference on Decision and Control. Part 4 (of 4)
CityKobe, Jpn

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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