A robust direct adaptive regulation architecture using dynamic neural network models

Author

Rovithakis, George A. ; Christodoulou, Manolis A.

Author_Institution

Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece

Volume

2

fYear

1994

fDate

2-5 Oct 1994

Firstpage

1110

Abstract

In this paper, we examine how dynamic neural network models can be used, in the development of direct robust adaptive regulators for unknown nonlinear dynamical systems. The modeling error term, whose presence is unavoidable, is not assumed to be a priori bounded. Rigorous mathematical analysis is given in order to analyze the stability properties of the closed loop system

Keywords

adaptive control; closed loop systems; control system analysis; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; robust control; uncertain systems; closed-loop system; dynamic neural network models; modeling error term; rigorous mathematical analysis; robust adaptive regulators; robust direct adaptive regulation architecture; stability; unknown nonlinear dynamical systems; Adaptive control; Backpropagation; Closed loop systems; Control systems; Force control; Linear feedback control systems; Mathematical analysis; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Regulators; Robustness; Sliding mode control; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2129-4

Type

conf

DOI

10.1109/ICSMC.1994.399992

Filename

399992