Design of Adaptive Kinematic Controller Using Radial Basis Function Neural Network for Trajectory Tracking Control of Differential-Drive Mobile Robot

被引：8
作者：
Tran Quoc Khai [1 ]
Ryoo, Young-Jae [2 ]
机构：
[1] Mokpo Natl Univ, Dept Elect Engn, Mokpo, South Korea
[2] Mokpo Natl Univ, Dept Elect & Control Engn, Mokpo, South Korea
来源：
INTERNATIONAL JOURNAL OF FUZZY LOGIC AND INTELLIGENT SYSTEMS | 2019年 / 19卷 / 04期
关键词：
Differential-drive mobile robot; Radial basis function neural network; Kinematic controller; Gradient descent method; Trajectory tracking;
D O I：
10.5391/IJFIS.2019.19.4.349
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this study, we propose an adaptive kinematic controller using a radial basis function neural network (RBFNN) to guide a differential-drive mobile robot (DDMR) during trajectory tracking. The kinematic controller is responsible for generating the reference values of the linear and angular velocities delivered to the robot actuators. With the fixed parameters of the kinematic controller, it is difficult for the robots to obtain acceptable performance. By using RBFNN and gradient descent method, the parameters of the kinematic controller can be updated online, thus providing smaller errors and better performance in applications. In this paper, the experimental results are presented to show the effectiveness of the proposed kinematic controller.
引用
收藏
页码：349 / 359
页数：11
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