A regulator design of dynamical systems with nonlinear uncertainties using multilayered neural networks

Author

Ohta, Hirobumi ; Yokota, Syuji

Author_Institution

Dept. of Aeronaut. Eng., Nagoya Univ., Japan

Volume

2

fYear

1993

fDate

25-29 Oct. 1993

Firstpage

1785

Abstract

Two control systems without using state measurements are proposed for compensating nonlinear modeling uncertainties. The designed controllers contain neural networks which provides estimates of the unknown states. Using the learning and nonlinear mapping capabilities of neural networks, the proposed control systems can be shown to accommodate a wider class of modeling uncertainties than the conventional LQR. Numerical examples are given to compare the design methods.

Keywords

compensation; control system synthesis; discrete time systems; linear systems; multilayer perceptrons; neurocontrollers; state estimation; uncertain systems; compensating; design methods; dynamical systems; learning; multilayered neural networks; nonlinear mapping capabilities; nonlinear uncertainties; regulator design; Control system synthesis; Control systems; Design methodology; Error correction; Multi-layer neural network; Neural networks; Nonlinear control systems; Regulators; State estimation; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on

Print_ISBN

0-7803-1421-2

Type

conf

DOI

10.1109/IJCNN.1993.717000

Filename

717000