Please use this identifier to cite or link to this item:
|Title:||Adaptation in the presence of a general nonlinear parameterization: An error model approach|
|Authors:||Loh, A.-P. |
|Source:||Loh, A.-P., Annaswamy, A.M., Skantze, F.P. (1999). Adaptation in the presence of a general nonlinear parameterization: An error model approach. IEEE Transactions on Automatic Control 44 (9) : 1634-1652. ScholarBank@NUS Repository. https://doi.org/10.1109/9.788531|
|Abstract:||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.|
|Source Title:||IEEE Transactions on Automatic Control|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 21, 2018
WEB OF SCIENCETM
checked on Jan 16, 2018
checked on Feb 18, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.