Self-Adaptive Robust Control of Joint Robots for Modeling Error

Article Preview

Abstract:

For joint robot system contains inevitable model error in the modeling process, an effective method is proposed for self-adaptive stability control in this paper. After building the robot dynamics model, error factors are analyzed in the model. Based on robust control theory, an improved self-adaptive PID controller is designed and its Lyapunov stability is verified. Finally, by simulation for a two-link manipulator, the result which shows the control method has well efficiency and practicality for robust stability control. The results will be significant for the precise control of the robot system.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

226-231

Citation:

Online since:

September 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] M.W. Spong, S. Hutchinson, M. Vidyasagar, Robot Modeling and Control, USA: John Wiley & Sons Inc., (2006).

Google Scholar

[2] H. Berghuis, H. Nijmeijer, P. Lohnberg, An addendum on Robust Control of Robots by the Computed Torque Method, Proc IEEE Confer Decision and Control, Brighton, England, pp.1049-1050, (1992).

DOI: 10.1016/0167-6911(92)90031-m

Google Scholar

[3] S.G. Shuzhi, T.H. Lee, C.J. Harris, Adaptive Neural Network Control of Robotic Manipulators , World IEEE Transactions on Industrial Electronics (S0278-0046), 44(6): 746-752, (1997).

DOI: 10.1109/41.649934

Google Scholar

[4] M. Boukattaya, T. Damak, M. Jallouli, Adaptive robust tracking control for mobile manipulators in the task-space under uncertainties, International Journal of Intelligent Computing and Cybernetics, 4(1): 81-92, (2011).

DOI: 10.1108/17563781111115804

Google Scholar

[5] K.S. Arendra, A.M. Annaswarny, A New Adaptine Law for Robust Adaptation Without Persiten Excitation, IEEE T-AC, 32(2): 134-145, (1987).

Google Scholar

[6] S.S. Ge, T.H. Lee, C.J. Harris, Adaptive Neural Network Control of Robotic Manipulators, World Scientific, (1998).

Google Scholar