A new stable tracking control scheme for robotic manipulators

被引：14

作者：

Feng, G

机构：

[1] Department of Systems and Control, School of Electrical Engineering, University of New South Wales, Sydney

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 1997年 / 27卷 / 03期

关键词：

convergence; neural networks; robots; simulation; tracking control;

D O I：

10.1109/3477.584957

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well-known computed torque method and a compensating controller. The compensating controller is realized by using a switch-type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis.

引用

页码：510 / 516

页数：7

共 35 条

[21] FEEDBACK-ERROR-LEARNING NEURAL NETWORK FOR TRAJECTORY CONTROL OF A ROBOTIC MANIPULATOR [J].