Title :
Adaptive RBF model for model-based control
Author :
Yu, D.L. ; Yu, D.W. ; Gomm, J.B. ; Page, G.F.
Author_Institution :
Sch. of Eng., Liverpool John Moores Univ., UK
Abstract :
An adaptive radial basis function (RBF) neural network model is developed for nonlinear systems dynamics using the recursive orthogonal least squares (ROLS) algorithm. The model is oriented to on-line control. A center bank is formed and its associated R matrix is updated on-line. A pruning method is used to select significant centers that used for prediction. The developed adaptive model is evaluated in real data modeling of a multivariable reactor rig and compared with a non-adaptive RBF model.
Keywords :
adaptive control; least squares approximations; matrix algebra; multivariable control systems; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; radial basis function networks; recursive estimation; R matrix; RBF neural network model; adaptive radial basis function model; model based control; multivariable reactor rig; nonlinear dynamical systems; online control; pruning method; recursive orthogonal least square algorithm; Adaptive control; Adaptive systems; Control systems; Inductors; Least squares methods; Neural networks; Nonlinear systems; Predictive models; Programmable control; Radial basis function networks;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1340489
