Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2011.2175946
Title: Data-based identification and control of nonlinear systems via piecewise affine approximation
Authors: Lai, C.Y.
Xiang, C. 
Lee, T.H. 
Keywords: Nonlinear systems
piecewise affine models
reference tracking
switching systems
system identification
weighted least squares
Issue Date: Dec-2011
Citation: Lai, C.Y., Xiang, C., Lee, T.H. (2011-12). Data-based identification and control of nonlinear systems via piecewise affine approximation. IEEE Transactions on Neural Networks 22 (12 PART 2) : 2189-2200. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2011.2175946
Abstract: The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA autoregressive exogenous (ARX) (autoregressive systems with exogenous inputs) models of nonlinear systems is proposed. Two key parameters defining a PWARX model, namely, the parameters of locally affine subsystems and the partition of the regressor space, are 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 PWARX model of the nonlinear system, a controller is then derived to control the system for reference tracking. Both simulation and experimental studies show that the proposed algorithm can indeed provide accurate PWA approximation of nonlinear systems, and the designed controller provides good tracking performance. © 2006 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/50887
ISSN: 10459227
DOI: 10.1109/TNN.2011.2175946
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