Volume 12, Issue 2 (Journal of Control, V.12, N.2 Summer 2018)                   JoC 2018, 12(2): 27-39 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Poorvaez N, Shojaei K. Designing an Output Feedback Tracking Controller for Mobile Manipulators by Using a Neural Adaptive Robust Technique. JoC 2018; 12 (2) :27-39
URL: http://joc.kntu.ac.ir/article-1-489-en.html
1- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Abstract:   (8577 Views)

In this paper, the tracking control of a robotic arm mounted on a wheeled mobile platform is considered. A nonlinear neural adaptive robust control algorithm is proposed for the output feedback tracking control of a wheeled mobile manipulator without measuring system velocities to deal with the unmodeled system dynamics, parametric uncertainties and external disturbances. A Lyapunov-based stability analysis shows that tracking and observation errors are Uniformly Ultimately Bounded (UUB) and converge to a small ball containing the origin. A Radial Basis Function Neural Network (RBFNN) is employed to compensate for the uncertainties of mobile manipulator dynamics. Nonparametric uncertainties and NN approximation errors are also compensated by an adaptive robust controller. In addition, hyperbolic tangent function is employed in the design of the output feedback controller to reduce the risk of actuators saturation and to produce smoother control signals. Finally, simulation results demonstrate the effectiveness of the proposed controller well.

Full-Text [PDF 936 kb]   (3543 Downloads)    
Type of Article: Research paper | Subject: Special
Received: 2017/06/13 | Accepted: 2018/01/3 | Published: 2018/10/3

1. Galicki. M., "An adaptive non-linear constraint control of mobile manipulators," Mechanism and Machine Theory, Vol. 88, pp. 63-85, 2015. [DOI:10.1016/j.mechmachtheory.2015.02.001]
2. Sun. W., Xia. J., "Adaptive control for mobile manipulators with affine constraints," 3rd International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS), pp. 354-357, 2016.
3. Shojaei. K., "Neural adaptive output feedback formation control of type (m,s) wheeled mobile robots," IET Control Theory and Applications, Vol. 11, No. 4, pp. 504-515, 2016. [DOI:10.1049/iet-cta.2016.0952]
4. Fang. M., Chen. W. and Li. Z., "Adaptive tracking control of coordinated nonholonomic mobile manipulators," Proceedings of 17th World Congress: The International Federation of Automatic Control, pp. 4343-4348, 2008.
5. Sun. W., Wu. Y Q., "Adaptive motion/force tracking control for a class of mobile manipulators," Asian Journal of Control, Vol. 17, No. 6, pp. 2409-2416, 2015. [DOI:10.1002/asjc.1122]
6. Wang. Y., Miao. Zh., Liu. L., Chen. Y., "Adaptive robust control of nonholonomic mobile manipulators with an application to condenser cleaning robotic systems," 8th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 358-363, 2013.
7. Li. Zh., Su. Ch., "Neural-Adaptive Control of Single-Master–Multiple-Slaves Teleoperation for Coordinated Multiple Mobile Manipulators With Time-Varying Communication Delays and Input Uncertainties," IEEE Transactions on Neural Networks and Learning Systems, Vol. 24, No. 9, pp. 1400-1413, 2013. [DOI:10.1109/TNNLS.2013.2258681] [PMID]
8. Chen. N., Yang. H., Han. X., Ai. Ch., Tang. Ch., Li. X., "Adaptive robust control of the mobile manipulator based on neural network," 11th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 2425-2430, 2016.
9. Xu. Y., Cao. X., Wang. Y., Gu. L., "The observer-based neural network adaptive robust control of underwater hydraulic manipulator," Conference on OCEANS'15 MTS/IEEE Washington, pp. 1-5, 2015.
10. White. G. D., Bhatt. R. M., Krovi. V.N., "Dynamic redundancy resolution in a nonholonomic wheeled mobile manipulator," Robotica, Vol. 25, No. 2, pp. 147-156, 2007. [DOI:10.1017/S0263574706003328]
11. Tzafestas. Spyros G., Introduction to Mobile Robot Control, 1st Edition, Elsevier Insights, 2013.
12. Polycarpou. M., "Stable adaptive neural control scheme for nonlinear systems," IEEE Transaction on Automatic Control, Vol. 41, No. 3, pp. 447-451, 1996. [DOI:10.1109/9.486648]
13. Khalil. Hassan K., Nonlinear Systems, Englewood Cliffs, Third Edition, Prentice Hall, NJ, 2002.
14. Xu. D., Zhao. D., Yi. J., Tan. X., "Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach," IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 39, No. 3, pp. 788-799, 2009. [DOI:10.1109/TSMCB.2008.2009464] [PMID]
15. Li. Z., Ge. S. S., Adams. M., Vijesoma. W. S., "Adaptive Robust Output-Feedback Motion/Force Control of Electrically Driven Nonholonomic Mobile Manipulators," IEEE Transactions on Control Systems Technology, Vol. 16, No. 6, pp. 1308-1315, 2008. [DOI:10.1109/TCST.2008.917228]
16. Ioannou. Petros, Fidan. Baris, Adaptive Control Tutorial, SIAM, Philadelphia, 2006.

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb