Title
Simultaneous Identification and Stabilization of Nonlinearly Parameterized Discrete-Time Systems by Nonlinear Least Squares Algorithm.
Abstract
This paper addresses the challenging problem of designing an adaptive feedback strategy for simultaneous identification and stabilization for a class of nonlinearly parameterized uncertain systems in discrete time. The Nonlinear Least Squares (NLS) algorithm is applied to estimate the unknown parameters, and it turns out to be the standard Least Squares (LS) algorithm whenever the model is linearly parameterized. Based on this algorithm, both the strong consistency of the estimator and the global stability of the system are achieved with the output feedback design for the scalar-parameter case.
Year
DOI
Venue
2016
10.1109/TAC.2015.2479879
IEEE Trans. Automat. Contr.
Keywords
Field
DocType
Algorithm design and analysis,Sensitivity,Uncertain systems,Standards,Least squares approximations,Adaptive control,Stochastic processes
Least squares,Mathematical optimization,Parameterized complexity,Algorithm design,Control theory,Algorithm,Iteratively reweighted least squares,Adaptive control,Non-linear least squares,Discrete time and continuous time,Mathematics,Recursive least squares filter
Journal
Volume
Issue
ISSN
61
7
0018-9286
Citations 
PageRank 
References 
2
0.37
21
Authors
2
Name
Order
Citations
PageRank
Chanying Li1638.44
Michael Z. Q. Chen283441.20