A new RBF network based sliding-mode control of nonlinear systems
Küçük Resim Yok
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network is a one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable convergence behavior is proved by Lyapunov stability approach. The parameter update is succeeded by a new strategy adapted from Levenberg-Marquardth (LM) method. The aim of construction of the proposed RBF network is to combine power of the networks which have different mapping abilities. These networks are auto-regressive exogenous input model, nonlinear static NN model and nonlinear dynamic NN model. To apply the model to control of the nonlinear systems, a known sliding mode control is applied to generate input of the system. From simulations; it is sown that the proposed network is an alternative model for identification and control of nonlinear systems with accurate results. © 2009 IEEE.
Açıklama
International Multiconference on Computer Science and Information Technology, IMCSIT '09 -- 12 October 2009 through 14 October 2009 -- Mragowo -- 79306
Anahtar Kelimeler
Online system identification and control, RBF network, Sliding mode control, Stable learning rate
Kaynak
Proceedings of the International Multiconference on Computer Science and Information Technology, IMCSIT '09
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
4
