Tracking control of multi-input affine nonlinear dynamical systems with unknown nonlinearities using dynamical neural networks

Author

Rovithakis, George A.

Author_Institution

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

Volume

29

Issue

2

fYear

1999

fDate

4/1/1999 12:00:00 AM

Firstpage

179

Lastpage

189

Abstract

The purpose of this paper is to design and rigorously analyze a tracking controller, based on a dynamic neural network model for unknown but affine in the control, multi input nonlinear dynamical systems, Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. The controller derived is smooth. No a priori knowledge of an upper bound on the “optimal” weights and modeling errors is required. Simulation studies are used, to illustrate and clarify the theoretical results

Keywords

Lyapunov methods; adaptive control; neural nets; nonlinear dynamical systems; stability; tracking; Lyapunov stability; dynamical neural networks; modeling errors; multi-input affine nonlinear dynamical systems; nonlinear dynamical systems; tracking control; tracking controller; tracking error; uniform ultimate boundedness property; unknown nonlinearities; upper bound; Control system analysis; Control systems; Error correction; Lyapunov method; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Signal analysis; Signal design; Tracking loops;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.752792

Filename

752792