Convergence of control performance by unfalsification of models—levels of confidence | Zendy

Veres S. M. | Zendy

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Convergence of control performance by unfalsification of models—levels of confidence

Author(s) -

Veres S. M.

Publication year - 2001

Publication title -

international journal of adaptive control and signal processing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.73

H-Index - 66

eISSN - 1099-1115

pISSN - 0890-6327

DOI - 10.1002/acs.685

Subject(s) - control theory (sociology) , controller (irrigation) , convergence (economics) , norm (philosophy) , nyquist plot , computer science , adaptive control , scheme (mathematics) , mathematics , mathematical optimization , control (management) , artificial intelligence , law , political science , mathematical analysis , chemistry , electrode , dielectric spectroscopy , agronomy , economics , electrochemistry , biology , economic growth

A general framework is introduced for iterative/adaptive controller design schemes by model unfalsification. An important feature of the schemes is their convergence near to the best possible controller given a set of model and controller structures. The problem of stability assured controller tuning is examined through unfalsified Riemannian bands of the Nyquist plot. Instability tolerant H ∞ and l 1 ‐norm‐based controller tuning schemes are introduced. Computational problems are discussed and a simulation is used to illustrate the new scheme. Copyright © 2001 John Wiley & Sons, Ltd.

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