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.
Document type :
Journal articles
Complete list of metadatas

http://hal.univ-grenoble-alpes.fr/hal-01637613
Contributor : Patricia Reynier <>
Submitted on : Friday, November 17, 2017 - 4:31:52 PM
Last modification on : Monday, April 9, 2018 - 12:22:39 PM

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

112