Geodesic
Robust neural network control of robotic manipulators via switching strategy
Kybernetika, Tome 51 (2015) no. 2, pp. 309-320 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical implementation is constructed. The proposed control scheme can guarantee that the resulting closed-loop switched system is asymptotically Lyapunov stable and the tracking error performance of the control system is well reached. Finally, a simulation example of two-link robotic manipulators is shown to illustrate the effectiveness of the proposed control method.
In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical implementation is constructed. The proposed control scheme can guarantee that the resulting closed-loop switched system is asymptotically Lyapunov stable and the tracking error performance of the control system is well reached. Finally, a simulation example of two-link robotic manipulators is shown to illustrate the effectiveness of the proposed control method.
DOI : 10.14736/kyb-2015-2-0309
Classification : 03C65, 20G40, 70E60, 93C85
Keywords: robotic manipulators; switching control strategy; RBF neural networks; multiple Lyapunov function 
@article{10_14736_kyb_2015_2_0309,
     author = {Yu, Lei and Fei, Shumin and Huang, Jun and Li, Yongmin and Yang, Gang and Sun, Lining},
     title = {Robust neural network control of robotic manipulators via switching strategy},
     journal = {Kybernetika},
     pages = {309--320},
     year = {2015},
     volume = {51},
     number = {2},
     doi = {10.14736/kyb-2015-2-0309},
     mrnumber = {3350564},
     zbl = {06487081},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2015-2-0309/}
}
TY  - JOUR
AU  - Yu, Lei
AU  - Fei, Shumin
AU  - Huang, Jun
AU  - Li, Yongmin
AU  - Yang, Gang
AU  - Sun, Lining
TI  - Robust neural network control of robotic manipulators via switching strategy
JO  - Kybernetika
PY  - 2015
SP  - 309
EP  - 320
VL  - 51
IS  - 2
UR  - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2015-2-0309/
DO  - 10.14736/kyb-2015-2-0309
LA  - en
ID  - 10_14736_kyb_2015_2_0309
ER  - 
%0 Journal Article
%A Yu, Lei
%A Fei, Shumin
%A Huang, Jun
%A Li, Yongmin
%A Yang, Gang
%A Sun, Lining
%T Robust neural network control of robotic manipulators via switching strategy
%J Kybernetika
%D 2015
%P 309-320
%V 51
%N 2
%U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2015-2-0309/
%R 10.14736/kyb-2015-2-0309
%G en
%F 10_14736_kyb_2015_2_0309
Yu, Lei; Fei, Shumin; Huang, Jun; Li, Yongmin; Yang, Gang; Sun, Lining. Robust neural network control of robotic manipulators via switching strategy. Kybernetika, Tome 51 (2015) no. 2, pp. 309-320. doi: 10.14736/kyb-2015-2-0309

[1] Barambones, O., Etxebarria, V.: Robust neural control for robotic manipulators. Automatica38 (2002), 235-242. | DOI | Zbl

[2] Bascetta, L., Rocco, P.: Revising the robust-control design for rigid robot manipulators. IEEE Trans. Robotics 26 (2010), 180-187. | DOI

[3] Du, H. B., He, Y. G., Cheng, Y. Y.: Finite-time cooperative tracking control for a class of second-order nonlinear multi-agent systems. Kybernetika 49 (2013), 507-523. | MR | Zbl

[4] Ge, S. S., Lee, T. H., Harris, C. J.: Adaptive Neural Network Control of Robotic Manipulators. World Scientific, London 1998. | DOI

[5] Han, T. T., S., Ge, S., Lee, T. T.: Adaptive neural control for a class of switched nonlinear systems. Systems Control Lett. 58 (2009), 109-118. | DOI | MR

[6] Imura, J., Sugie, T., Yoshikawa, T.: Adaptive robust control of robot manipulators-theory and experiment. IEEE Trans. Robot. Automatic 10 (1994), 705-710. | DOI

[7] Krstic, M., Kanellakopoulos, I., V.Kokotovic, P.: Nonlinear and Adaptive Control Design. Wiley, New York 1995.

[8] Lan, J. L., Sun, W. J., Peng, Y. J.: Constrained robust adaptive stabilization for a class of lower triangular systems with unknown control direction. Kybernetika 50 (2014), 450-469. | DOI | MR | Zbl

[9] Lewis, F. L., Abdallah, C. T., Dawson, D. M.: Control of Robot Manipulators. MacMillan, New York 1993.

[10] Liberzon, D.: Switching in Systems and Control. Birkhauser, Boston 2003. | DOI | MR | Zbl

[11] Long, F., Fei, S.: Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems. Neurocomputing 71 (2008), 1741-1747. | DOI

[12] Salas, O., Castaneda, H., Leon-Morales, J. De: Attitude observer-based robust control for a twin rotor system. Kybernetika 49 (2013), 809-828. | MR | Zbl

[13] Slotine, J. J., Li, W. P.: Applied Nonlinear Control. Prentice Hall, Englewood Cliffs, New Jersey 1991. | Zbl

[14] Sun, Z. D., Ge, S. S.: Analysis and synthesis of switched linear control systems. Automatica 41 (2005), 181-195. | DOI | MR | Zbl

[15] Tomei, P.: Robust adaptive friction compensation for tracking control of robot manipulators. IEEE Trans. Automat. Control 45 (2000), 2164-2169. | DOI | MR | Zbl

[16] Wang, X. H., Ji, H. B., Wang, C. R.: Distributed output regulation for linear multi-agent systems with unknown leaders. Kybernetika 49 (2013), 524-538. | MR | Zbl

[17] Wang, L., Chai, T.: Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics. IEEE Trans. Industr. Electronics 56 (2009), 3296-3304. | DOI

[18] Wang, L., Chai, T., Yang, C.: Neural-network-based contouring control for robotic manipulators in operational space. IEEE Transactions on Control Systems Technology 20 (2012),1073-1080. | DOI

[19] Xie, G. M., Wang, L.: Periodic stabilizability of switched linear control systems. Automatica 45 (2009), 2141-2148. | DOI | MR | Zbl

[20] Yu, L., Fei, S. M., Sun, L. N., Huang, J., Yang, G.: Design of robust adaptive neural switching controller for robotic manipulators with uncertainty and disturbances. J. Intell. Robot. Systems 77 (2015), 571-581. | DOI

Cité par Sources :