Decentralized adaptive neural network control for reconfigurable manipulators

Author

Zhu, Lu ; Li, Yuanchun

Author_Institution

State Key Lab. of Automobile Dynamic Simulation, Jilin Univ., Changchun, China

fYear

2010

fDate

26-28 May 2010

Firstpage

1760

Lastpage

1765

Abstract

In this paper, a decentralized adaptive neural network control algorithm for reconfigurable manipulators based on Lyapunov´s stability analysis and backstepping techniques is proposed. The dynamics of reconfigurable manipulators is represented as a set of interconnected subsystems. Neural networks are used to approximate the unknown dynamic functions and interconnections in the subsystems by using adaptive algorithm. The effectiveness of the proposed scheme is demonstrated by computer simulations.

Keywords

Lyapunov methods; adaptive control; decentralised control; interconnected systems; manipulators; neurocontrollers; stability; Lyapunov stability analysis; backstepping techniques; decentralized adaptive neural network control; dynamic functions; interconnected subsystems; reconfigurable manipulators; Adaptive algorithm; Adaptive control; Adaptive systems; Algorithm design and analysis; Backstepping; Computer simulation; Lyapunov method; Manipulator dynamics; Neural networks; Programmable control; Adaptive Control; Backstepping Design; Decentralized Control; Neural Networks; Reconfigurable Manipulators;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498523

Filename

5498523