Diagonal recurrent neural network-based control: convergence and stability

Author

Ku, Chao Chee ; Lee, Kwang Y.

Author_Institution

Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA

Volume

3

fYear

1994

fDate

29 June-1 July 1994

Firstpage

3340

Abstract

Convergence and the closed-loop stability property are established for a diagonal recurrent neural network (DRNN) based control system. Two DRNNs are utilized in the control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system and since it is not fully connected, the architecture is simpler than a fully connected recurrent neural network. Convergence theorems for the adaptive DBP algorithms are developed and the closed-loop stability is established for the DRNN based control system when the plant is BIBO stable.

Keywords

backpropagation; closed loop systems; convergence; identification; neurocontrollers; recurrent neural nets; stability; closed-loop stability; convergence; diagonal recurrent neural network; diagonal recurrent neurocontroller; diagonal recurrent neuroidentifier; dynamic backpropagation algorithm; Artificial neural networks; Backpropagation algorithms; Chaos; Control systems; Convergence; Fuzzy control; Neural networks; Neurons; Recurrent neural networks; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1994

Print_ISBN

0-7803-1783-1

Type

conf

DOI

10.1109/ACC.1994.735193

Filename

735193