Composite adaptation for neural network-based controllers

Author

Patre, Parag M. ; Bhasin, Shubhendu ; Wilcox, Zachary D. ; Dixon, Warren E.

Author_Institution

Dept. of Mech. & Aerosp. Eng., Univ. of Florida, Gainesville, FL, USA

fYear

2009

fDate

15-18 Dec. 2009

Firstpage

6726

Lastpage

6731

Abstract

This paper presents a novel approach to design a composite adaptation law for neural networks that uses both the system tracking errors and a prediction error containing parametric information by devising an innovative swapping procedure that uses the recently developed Robust Integral of the Sign of the Error (RISE) feedback method. Semi-global asymptotic tracking is proven for an Euler-Lagrange system.

Keywords

Lyapunov methods; adaptive systems; asymptotic stability; feedback; neurocontrollers; robust control; Euler-Lagrange system; composite adaptation; neural network-based controller; parametric information; prediction error; robust integral of the sign of the error feedback method; semiglobal asymptotic tracking; swapping procedure; system tracking error; Adaptive control; Asymptotic stability; Control systems; Error correction; Feedback; Neural networks; Neurofeedback; Robust control; Sliding mode control; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on

Conference_Location

Shanghai

ISSN

0191-2216

Print_ISBN

978-1-4244-3871-6

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2009.5400453

Filename

5400453