Generalized predictive control based on error correction using the dynamic neural network

Author

Xiaohua, Liu ; Xiuhong, Wang ; Wane Yunge

Author_Institution

Yantai Teachers Univ., China

Volume

3

fYear

2000

fDate

2000

Firstpage

1863

Abstract

Considering the influence of modelling error on the robustness of nonlinear predictive control, the paper proposes a generalized predictive control algorithm based on error correction using the dynamic backpropagation network, the algorithm has dynamic compensation capability so that the dynamic error of the model can be effectively reduced and the modelling accuracy can be raised. The simulation results show the algorithm is effective for nonlinear systems

Keywords

backpropagation; compensation; error correction; neurocontrollers; nonlinear control systems; predictive control; robust control; dynamic backpropagation network; dynamic compensation capability; dynamic error; dynamic neural network; error correction; generalized predictive control; modelling accuracy; modelling error; nonlinear predictive control; robustness; Error correction; Heuristic algorithms; Neodymium; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Predictive control; Predictive models; Robust control; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.862798

Filename

862798