A hierarchical neuro-fuzzy system to near optimal-time trajectory planning of redundant manipulators

被引：15

作者：

Khoukhi, Amar ^{[2
]}

Baron, Luc ^{[2
]}

Balazinski, Marek ^{[2
]}

Demirli, Kudret ^{[1
]}

机构：

[1] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada

[2] Ecole Polytech, Dept Mech Engn, Montreal, PQ H3C 3A7, Canada

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2008年 / 21卷 / 07期

关键词：

Robot manipulators; Multi-objective trajectory planning; Minimum-time control; Hierarchical neuro-fuzzy systems;

D O I：

10.1016/j.engappai.2007.12.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-of-freedom planar manipulator using a hierarchical hybrid neuro-fuzzy system. A first neuro-fuzzy network named NeFIK is considered to solve the inverse kinematics problem. After a few pre-processing steps characterizing the minimum-time trajectory and the corresponding torques, a second neuro-fuzzy controller is built. Its purpose is to fit the robot dynamic behavior corresponding to the determined minimum-time trajectory with respect to actuators models, torque nominal values, as well as position, velocity, acceleration and jerk boundary conditions. A Tsukamoto Neuro-Fuzzy Inference network is designed to achieve the online control of the robot. The premise parameters (antecedent membership functions parameters) as well as rule-consequence parameters arc learned and optimized, generating the optimal-time trajectory torques, representing the robot dynamic behavior. Simulation results are presented and discussed. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：974 / 984

页数：11

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