Adaptive control of nonlinear non-minimum phase systems using neural networks

Author

Yue, Heng ; Chai, Tianyou ; Shao, Cheng

Author_Institution

Res. Center of Autom., Northeast Univ. of Technol., Shenyang, China

Volume

3

fYear

1997

Firstpage

2211

Abstract

A novel technique, using neural networks, is proposed for the adaptive control of a class of nonlinear nonminimum phase systems. Using Taylor expansion, the nonlinear system can be regarded as a linear nonminimum phase system with a measurable disturbance. Pole-placement is used to stabilize the system, and a neural network is used to approximate the nonlinear term. Feedforward compensation is used to eliminate steady tracking errors which are caused by the nonlinear term

Keywords

adaptive control; compensation; discrete time systems; feedforward; linearisation techniques; neurocontrollers; nonlinear systems; pole assignment; series (mathematics); SISO systems; Taylor expansion; adaptive control; discrete time systems; feedforward compensation; linearisation; neural networks; nonlinear systems; nonminimum phase systems; pole-placement; tracking errors; Adaptive control; Automation; Electronic mail; Equations; Error correction; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Phase measurement; Taylor series;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.657100

Filename

657100