A robust optimal design for strictly positive realness in recursive parameter adaptation

Hui Xiao 1 Ioan Doré Landau 2 Xu Chen 1
2 GIPSA-SLR - SLR
GIPSA-DA - Département Automatique
Abstract : This paper provides an optimization-based approach to assure the strict positive real (SPR) condition in a set of recursive parameter adaptation algorithms (PAA). The developed algorithms and tools enable a multiobjective formulation of the SPR problem, creating new controls of the stability and parameter convergence in PAAs. In addition to assuring the SPR condition for global stability in PAAs, we provide an algorithmic solution for uniform convergence under performance constraints in PAAs. Several new aspects of parameter convergence were observed with the adoption of the algorithm in a narrow-band identification problem. The proposed algorithm is verified in simulation and experiments on a precision motion control platform in advanced manufacturing.
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Article dans une revue
International Journal of Adaptive Control and Signal Processing, Wiley, 2017, 31 (8), pp.1205 -1216. 〈10.1002/acs.2757〉
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http://hal.univ-grenoble-alpes.fr/hal-01637613
Contributeur : Patricia Reynier <>
Soumis le : vendredi 17 novembre 2017 - 16:31:52
Dernière modification le : jeudi 11 janvier 2018 - 06:26:25

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Hui Xiao, Ioan Doré Landau, Xu Chen. A robust optimal design for strictly positive realness in recursive parameter adaptation. International Journal of Adaptive Control and Signal Processing, Wiley, 2017, 31 (8), pp.1205 -1216. 〈10.1002/acs.2757〉. 〈hal-01637613〉

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