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

Authors:
Xia Hong;Sheng Chen
Affiliations:
School of Systems Engineering, University of Reading, Reading RG6 6AY, UK;School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK and Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Venue:
Neurocomputing
Year:
2012

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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 a particle swarm optimization (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.