Paper Info
Title
Neural network-based sliding mode adaptive control for robot manipulators
Abstract
This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.
Year
DOI
Venue
2011
10.1016/j.neucom.2011.03.015
Neurocomputing
Keywords
Field
DocType
lyapunov theory,mode adaptive output feedback,robust trajectory tracking problem,trajectory tracking,observer estimation error,mode adaptive control,nn-based adaptive observer,sliding mode adaptive control,tracking error asymptotically converge,robot manipulators,output feedback control,adaptive technique,neural network (nn),robot manipulator,neural network,adaptive control
State observer,Lyapunov function,Control theory,Adaptive control,Artificial neural network,Observer (quantum physics),Trajectory,Mathematics,Tracking error,Sliding mode control
Journal
Volume
Issue
ISSN
74
14-15
Neurocomputing
Citations 
PageRank 
References 
28
1.00
8
Authors
5
Name
Order
Citations
PageRank
Tairen Sun11359.17
Hai-Long Pei2527.34
Yongping Pan366037.53
Hongbo Zhou4291.35
Caihong Zhang5533.32