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Stable worst‐case iterative identification and control redesign via unfalsification
Author(s) -
Xia Hao
Publication year - 1999
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/(sici)1099-1115(199911)13:7<573::aid-acs573>3.0.co;2-6
Subject(s) - a priori and a posteriori , scheme (mathematics) , identification (biology) , convergence (economics) , controller (irrigation) , computer science , selection (genetic algorithm) , identification scheme , iterative learning control , mathematical optimization , set (abstract data type) , iterative method , laguerre polynomials , control theory (sociology) , control (management) , mathematics , artificial intelligence , data mining , mathematical analysis , philosophy , programming language , botany , epistemology , agronomy , economics , biology , measure (data warehouse) , economic growth
This work is a further development of a recently introduced iterative identification and control redesign scheme by Veres which is based on the concept of model unfalsification. In this scheme convergence of the iterations and attainment of nearly optimal worst‐case control performance is ensured under the a priori given set of controller structures. This paper makes two contributions: proposes the use of Laguerre and Kautz models and introduces caution into the iterative steps of this scheme similarly as it was done by Anderson et al. An advantage will be that structure selection of the models will be based on control related criteria, hence model structure selection will be intrinsic to the iterative scheme. Copyright © 1999 John Wiley & Sons Ltd.