Design of Neural Network-based Backstepping Controller for the Folding-Boom Aerial Platform Vehicle

DOI

10.2991/meic-15.2015.63How to use a DOI?

Keywords

aerial platform vehicle; model uncertainties; trajectory tracking control; backstepping controller; neural network

Abstract

In this paper, the robust trajectory tracking problem is addressed for the work platform of folding-boom aerial platform vehicle in the presence of uncertainties and disturbances. The control objective is to make the work platform move along a desired reference trajectory and make the vibration inhibit at the same time. Since neural network system can approximate any nonlinear function with arbitrary accuracy over a compact set in the light of the universal approximation theorem, a neural network-based backstepping controller, which composed of backstepping control and neural network, is proposed for the trajectory tracking control of the work platform in the case of modeling uncertainties and disturbances. According to Lyapunov stability theorem, the stability and convergence of the overall system can be guaranteed by the derived control law. In addition, simulation results demonstrate that the proposed controller is effective for suppressing the vibration and reducing trajectory tracking error of the work platform.

Copyright

© 2015, 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  - Haidong Hu
AU  - Yan Ren
AU  - Huibo Liu
PY  - 2015/04
DA  - 2015/04
TI  - Design of Neural Network-based Backstepping Controller for the Folding-Boom Aerial Platform Vehicle
BT  - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 265
EP  - 268
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-15.2015.63
DO  - 10.2991/meic-15.2015.63
ID  - Hu2015/04
ER  -