CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Adaptive neural network tracking control for manipulators with uncertainties
Long Cheng; Zeng-Guang Hou; Min Tan; H. Wang
2008
Conference Namethe 17th World Congress The International Federation of Automatic Contro
Conference DateJuly 6-11, 2008
Conference PlaceSeoul
CountrySouth Korea
AbstractAn adaptive neural network controller is proposed to deal with the end-effector tracking problem of manipulators with uncertainties. By employing the adaptive Jacobian scheme, neural networks, and backstepping technique, the torque controller can be obtained which is demonstrated to be stable by the Lyapunov approach. The updating laws for designed controller parameters are derived by the projection method, and the tracking error can be reduced as small as possible. The favorable features of the proposed controller lie in that: (1) the uncertainty in manipulator kinematics is taken into account; (2) the “linearity-in-parameters” assumption for the uncertain terms in dynamics of manipulators is no longer necessary; (3) effects of external disturbances are considered in the controller design. Finally, the satisfactory performance of the proposed approach is illustrated by simulation results on a PUMA 560 robot.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23160
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Long Cheng,Zeng-Guang Hou,Min Tan,et al. Adaptive neural network tracking control for manipulators with uncertainties[C],2008.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Long Cheng]'s Articles
[Zeng-Guang Hou]'s Articles
[Min Tan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Long Cheng]'s Articles
[Zeng-Guang Hou]'s Articles
[Min Tan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Long Cheng]'s Articles
[Zeng-Guang Hou]'s Articles
[Min Tan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.