On the neuro-genetic approach for determining optimal control of a rotary crane

Author

Rekik, Chokri ; Djemel, Mohamed ; Derbel, Nabil

Author_Institution

Res. Unit on Intelligent Control, Design & Optimisation of Complex Syst., Univ. of Sfax, Tunisia

Volume

1

fYear

2003

fDate

23-25 June 2003

Firstpage

124

Abstract

The aim of this paper considers the determination of optimal control trajectories of a complex process. The proposed method is based on the decomposition of the system into interconnected subsystems. We consider the cases where subsystems are linear in terms of their state and control vectors. For this reason, a neural network is identified which compute local gains. Genetic algorithms are used to optimize the networks weights. Simulation results show that the proposed approximations yield satisfactory performances.

Keywords

cranes; feedforward neural nets; genetic algorithms; interconnected systems; neurocontrollers; nonlinear systems; optimal control; vectors; control vectors; genetic algorithms; interconnected subsystems; multilayer neural nets; network weights; neurogenetics; nonlinear systems; optimal control; optimization; rotary crane; state vectors; system decomposition; Artificial neural networks; Biological cells; Cranes; Genetic algorithms; Intelligent control; Neural networks; Nonlinear systems; Optimal control; Riccati equations; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on

Print_ISBN

0-7803-7729-X

Type

conf

DOI

10.1109/CCA.2003.1223276

Filename

1223276