The unfalsified control concept and learning

Author

Safonov, Michael G. ; Tsao, Tung-Ching

Author_Institution

Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA

Volume

42

Issue

6

fYear

1997

fDate

6/1/1997 12:00:00 AM

Firstpage

843

Lastpage

847

Abstract

Without a plant model or other prejudicial assumptions, a theory is developed for identifying control laws which are consistent with performance objectives and past experimental data-possibly before the control laws are ever inserted in the feedback loop. The theory complements model-based methods such as H^∞-robust control theory by providing a precise characterization of how the set of suitable controllers shrinks when new experimental data is found to be inconsistent with prior assumptions or earlier data. When implemented in real time, the result is an adaptive switching controller. An example is included

Keywords

adaptive control; control system synthesis; feedback; identification; learning systems; H^∞-robust control theory; adaptive switching controller; control law identification; experimental data; feedback loop; learning; performance objectives; unfalsified control concept; Adaptive control; Control systems; Control theory; Feedback loop; Learning systems; Nonlinear systems; Open loop systems; Programmable control; Robust control; Uncertainty;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.587340

Filename

587340