Direct Adaptive Control Based on Improved RBF Neural Network for Omni-directional Mobile Robot

DOI

10.2991/meic-15.2015.252How to use a DOI?

Keywords

direct adaptive controller; radial basis function neural network; omni-directional mobile robot; nonlinear and uncertain nonlinear system; Lyapunov stability theory

Abstract

A direct adaptive controller based on improved radial basis function (RBF) neural networks (NN) is proposed for an omni-directional mobile robot (OMR). The OMR is a multi-input and multi-output (MIMO), unmodeled and uncertain nonlinear system which is difficult to be modeled due to a large number of immeasurable and uncertain variables. To model the system exactly and increase the real-time performance, a novel direct adaptive control approach based on improved RBF-NN is designed to approximate the OMR, which needs no explicit knowledge of the uncertain nonlinear MIMO system. Besides the kinematics, the dynamics of the OMR are considered to perform tasks with heavy load transportations or high speed movements. A stable on-line adaptive law is derived and proved using Lyapunov stability theory. The proposed controller is applied the OMR trajectory tracking and shows excellent robustness and stability. The simulation results demonstrate the feasibility and validity of proposed scheme.

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  - Jinhui Fan
AU  - Songmin Jia
AU  - Xiuzhi Li
PY  - 2015/04
DA  - 2015/04
TI  - Direct Adaptive Control Based on Improved RBF Neural Network for Omni-directional Mobile Robot
BT  - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 1108
EP  - 1112
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-15.2015.252
DO  - 10.2991/meic-15.2015.252
ID  - Fan2015/04
ER  -