期刊
AUTOMATICA
卷 95, 期 -, 页码 548-551出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2018.06.011
关键词
Parameter estimation; Observer design theory; Adaptive systems; Nonlinear systems
资金
- Ministry of Education and Science of Russian Federation [14.Z50.31.0031, 8.8885.2017/8.9]
- NSFC [61473183, U1509211]
Dynamic regressor extension and mixing is a new technique for parameter estimation with guaranteed performance improvement - with respect to classical gradient or least-squares estimators - that has proven instrumental in the solution of several open problems in system identification and adaptive control. In this brief note we give two interpretations of this parameter estimator in terms of the recent extensions, to the cases of nonlinear systems and observation of linear functionals for time-varying systems, of the classical Luenberger's state observers. (C) 2018 Elsevier Ltd. All rights reserved.
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