Adaptation in the presence of a general nonlinear parameterization: an error model approach

Parametric uncertainties in adaptive estimation and control have been dealt with, by and large, in the context of linear parameterizations. Algorithms based on the gradient descent method either lead to instability or inaccurate performance when the unknown parameters occur nonlinearly. Complex dyna...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 44; no. 9; pp. 1634 - 1652
Main Authors Ai-Poh Loh, Annaswamy, A.M., Skantze, F.P.
Format Journal Article
LanguageEnglish
Published IEEE 01.09.1999
Subjects
Adaptation
Adaptive control
Adaptive control systems
Adaptive estimation
Algorithms
Context modeling
Control systems
Convergence
Error correction
Errors
Mathematical models
Nonlinear dynamical systems
Nonlinear equations
Nonlinearity
Parametrization
Stability
Uncertainty
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Summary:Parametric uncertainties in adaptive estimation and control have been dealt with, by and large, in the context of linear parameterizations. Algorithms based on the gradient descent method either lead to instability or inaccurate performance when the unknown parameters occur nonlinearly. Complex dynamic models are bound to include nonlinear parameterizations which necessitate the need for new adaptation algorithms that behave in a stable and accurate manner. The authors introduce, in this paper, an error model approach to establish these algorithms and their global stability and convergence properties. A number of applications of this error model in adaptive estimation and control are included, in each of which the new algorithm is shown to result in global boundedness. Simulation results are presented which complement the authors' theoretical derivations.
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content type line 23
ISSN:0018-9286
1558-2523
DOI:10.1109/9.788531