Neural network based robust tracking control for nonholonomic mobile robotic system

DOI

10.2991/eeeis-16.2017.101How to use a DOI?

Keywords

RBF neural network; Computed torque control; control; Lyapunov stability.

Abstract

A hybrid tracking control scheme which combines RBF neural network with nonlinear method is proposed. RBF neural network is designed to approximate the system uncertainty terms, and control is utilized to achieve a desired robust tracking performance. Based on Lyapunov theory, the tracking errors of the closed-loop system are bounded. Besides, a specified tracking performance is obtained by the proposed robust hybrid control even though the disturbances are merely integral bounded. Compared the proposed method with the computed torque control under the uncertainties and external disturbances, simulation experiments show the effectiveness of the proposed control strategy.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ying-Nan Bian
AU  - Jin-Zhu Peng
PY  - 2016/12
DA  - 2016/12
TI  - Neural network based robust tracking control for nonholonomic mobile robotic system
BT  - Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016)
PB  - Atlantis Press
SP  - 816
EP  - 821
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-16.2017.101
DO  - 10.2991/eeeis-16.2017.101
ID  - Bian2016/12
ER  -