Title :
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
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;
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
DOI :
10.1109/CCDC.2010.5498523
