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A robust optimal design for strictly positive realness in recursive parameter adaptation
Author(s) -
Xiao Hui,
Landau Ioan D.,
Chen Xu
Publication year - 2017
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.2757
Subject(s) - convergence (economics) , stability (learning theory) , adaptation (eye) , mathematical optimization , identification (biology) , computer science , set (abstract data type) , control theory (sociology) , algorithm , control (management) , mathematics , artificial intelligence , machine learning , physics , botany , optics , economics , biology , programming language , economic growth
Summary 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.