Adaptive control based on neural network for uncertain space robot

Zhiyong Tang; Shao He; Mingyi Yang; Zhongcai Pei

doi:10.1117/12.906087

29 October 2011 Adaptive control based on neural network for uncertain space robot

Zhiyong Tang, Shao He, Mingyi Yang, Zhongcai Pei

Proceedings Volume 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization; 82052Z (2011) https://doi.org/10.1117/12.906087
Event: 2011 International Conference on Photonics, 3D-imaging, and Visualization, 2011, Guangzhou, China

Abstract

An adaptive PID control method based on RBF neural network for the free-floating dual-arm space robot's uncertain system model is proposed in this paper. Bring about the precise control of joints and carrier by the design of the control algorithm for space robot. Compared to traditional control method, its adaptive ability is stronger and requirement for the linearization of dynamic equation's inertial parameters is weak. We put it compared to the adaptive PID control method which is a traditional control methods .Having simulation in the case of luffing, frequency conversion and posture intrusive. The results of experimental demonstrate the feasibility and practicability of this method.

Citation Download Citation

Zhiyong Tang, Shao He, Mingyi Yang, and Zhongcai Pei "Adaptive control based on neural network for uncertain space robot", Proc. SPIE 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization, 82052Z (29 October 2011); https://doi.org/10.1117/12.906087

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available