Open AccessChaos control of Lorenz system via RBF neuralnetwork sliding mode controller
Author(s) -
Huijun Guo,
Junhua Liu
Publication year - 2004
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.53.4080
Subject(s) - control theory (sociology) , parametric statistics , sliding mode control , robustness (evolution) , radial basis function , computer science , artificial neural network , controller (irrigation) , nonlinear system , mathematics , physics , artificial intelligence , control (management) , biochemistry , statistics , chemistry , quantum mechanics , biology , agronomy , gene
A novel adaptive radial basis function(RBF) neural network sliding mode strat egy is developed to con trol Lorenz chaos with parametric uncertainties and external disturbances. Based on the controllable canonical form of system state error at its unstable equili brium, a sliding surface is defined as the only input to the RBF controller. Onl y seven RBFs are required for the controller and their weights ar e trained on-line based on the sliding surface approaching condition. The simula tio n results show that this method is feasible and effective, and the robustness to parametric uncertainties and external disturbance is provided.