Title :
Nonlinear reconfigurable control based on RBF neural networks
Author :
Zhou Chuan ; Weili, Hu ; Qingwei, Chen ; Yong, Wang ; Shousong, Hu
Author_Institution :
Dept. of Autom., Nanjing Univ. of Sci. & Technol., China
Abstract :
A new type of nonlinear reconfigurable control strategy based on model-following method using radial basis function (RBF) neural networks is presented in this paper. This method can make the outputs of an impaired system track those of reference model accurately without knowing the location and damage degree of failure, and a RBF neural network controller is used to compensate nonlinear dynamics caused by failure; simulation results reveal that this method has good reconfigurable performance and robustness
Keywords :
compensation; model reference adaptive control systems; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; radial basis function networks; robust control; RBF neural networks; impaired system tracking; model-following method; nonlinear dynamics compensation; nonlinear reconfigurable control; radial basis function neural networks; robustness; Aerodynamics; Automatic control; Automation; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Robust control; Space technology;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863385
