BLDC of Robotic Manipulators with Neural Torque Compensator based Optimal Robust Control

被引：0

作者：

Belov, Mikhail P. ^{[1
]}

Tran Dang Khoa ^{[1
]}

Dinh Dang Truong ^{[1
]}

机构：

[1] St Petersburg Electrotech Univ LETI, Dept Robot & Ind Automat, St Petersburg, Russia

来源：

PROCEEDINGS OF THE 2019 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (EICONRUS) | 2019年

关键词：

Optimal robust control Theta-D; neural compensator; artificial neural network;

D O I：

10.1109/eiconrus.2019.8656779

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a neural torque compensator for robotic manipulator control based on optimal robust control in the presence of nonlinear uncertain unknown load in system parameters. The first section introduces mathematical model of robotic manipulator electric driver with nonlinear parameters. In the next section, the structures of the neural compensated torque based on optimal robust control Theta-D are analyzed. The quality criterion in control system is analyzed with the combination of nonlinear optimal compensated torque based on linear quadratic Gaussian algorithm and neural torque compensator. Optimal proportional-integral regulators are developed in speed loop and position loop of manipulator control system, which improve dynamic characteristics.

引用

页码：437 / 441

页数：5

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