Please use this identifier to cite or link to this item:
|Title:||Identification and control of nonlinear systems via piecewise affine approximation|
|Citation:||Lai, C.Y., Xiang, C., Lee, T.H. (2010). Identification and control of nonlinear systems via piecewise affine approximation. Proceedings of the IEEE Conference on Decision and Control : 6395-6402. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2010.5717032|
|Abstract:||Piecewise affine model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the piecewise affine ARX models of nonlinear systems is proposed. Two key parameters defining a piecewise affine ARX model, namely the parameters of locally affine subsystems and the partition of the regressor space, will be estimated, the former through a least-squares based identification method using multiple models, and the latter using standard procedures such as neural network classifier or support vector machine classifier. Having obtained the piecewise affine ARX model of the nonlinear system, a controller is then derived to control the system for reference tracking. Simulation studies show that the proposed algorithm can indeed provide accurate piecewise affine approximation of nonlinear systems, and the designed controller provides good tracking performance. ©2010 IEEE.|
|Source Title:||Proceedings of the IEEE Conference on Decision and Control|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 13, 2018
WEB OF SCIENCETM
checked on Jun 5, 2018
checked on Apr 21, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.