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The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization
The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization
In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using particle swarmoptimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.
0925-2312
216-223
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Hong, Xia and Chen, Sheng (2012) The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization. Neurocomputing, 82, 216-223.

Record type: Article

Abstract

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using particle swarmoptimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

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Published date: April 2012
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 273151
URI: http://eprints.soton.ac.uk/id/eprint/273151
ISSN: 0925-2312
PURE UUID: 8d1cc2ff-5726-4075-9132-5dfe5abec4a9

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Date deposited: 31 Jan 2012 20:05
Last modified: 16 Dec 2019 20:39

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Contributors

Author: Xia Hong
Author: Sheng Chen

University divisions

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