A Neural Fuzzy Inference Based Adaptive Controller Using Learning Process for Nonholonomic Robots

被引：0

作者：

Wang, Ting ^{[1
]}

Gautero, Fabien ^{[1
]}

Sabourin, Christophe ^{[1
]}

Madani, Kurosh ^{[1
]}

机构：

[1] Univ Paris, EST Creteil UPEC, Images Signals & Intelligence Syst Lab LISSI EA39, Senart FB Inst Technol, F-77127 Lieusaint, France

来源：

ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2011, PT I | 2011年 / 6691卷

关键词：

Neuro-Fuzzy; Learning process; Nonholonomic robot; Controller; Real-Time; SYSTEMS; STABILIZATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a control strategy for a nonholonomic robot which is based on using the learning process of an Adaptive Neural Fuzzy Inference System (ANFIS). The proposed neuro-controller allows the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations' results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

引用

页码：65 / 72

页数：8

共 12 条

[1] Motion planning for formations of mobile robots [J].