Simultaneous Identification and Stabilization of Nonlinearly Parameterized Discrete-Time Systems by Nonlinear Least Squares Algorithm

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 parame...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2016-07, Vol.61 (7), p.1810-1823
Main Authors: Chanying Li, Chen, Michael Z. Q.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive systems
Algorithm design and analysis
Algorithms
Automatic control
disturbances
Estimates
Feedback
Identification
Least squares approximations
Least squares method
nonlinear least squares
Nonlinearity
nonlinearly parameterized systems
Sensitivity
Stabilization
Stochastic processes
Uncertain systems
uncertainties
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Summary: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.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2015.2479879