Title :
A model-following adaptive controller using radial basis function networks
Author :
Hoya, T. ; Ishida, Yuuki
Abstract :
In this paper, we propose a method to design a model-following adaptive controller using radial basis function networks (RBF-NNs). The method is very simple to implement by exploiting the properties of RBF-NNs. The proposed method identifies linear or nonlinear plants and implements a stable model-following adaptive controller by utilizing identification results. Simulation results show the effectiveness of the proposed control schemes.
Keywords :
control system synthesis; identification; model reference adaptive control systems; neurocontrollers; nonlinear control systems; radial basis function networks; controller design; identification results; linear plants; model-following adaptive controller; nonlinear plants; radial basis function networks; stable model-following adaptive controller; Adaptive control; Biological neural networks; Control systems; Design methodology; Equations; Neural networks; Neurons; Programmable control; Radial basis function networks; Vectors;
Conference_Titel :
Control Applications, 2002. Proceedings of the 2002 International Conference on
Print_ISBN :
0-7803-7386-3
DOI :
10.1109/CCA.2002.1038706
