MODEL-FREE ONLINE REINFORCEMENT LEARNING OF A ROBOTIC MANIPULATOR

被引：3

作者：

Sweafford, Jerry, Jr. ^{[1
]}

Fahimi, Farbod ^{[1
]}

机构：

[1] UAH, Dept Mech & Aerosp Engn MAE, Huntsville, AL 35899 USA

来源：

MECHATRONIC SYSTEMS AND CONTROL | 2019年 / 47卷 / 03期

关键词：

Reinforcement learning; neural networks; trajectory tracking; model-free; control; robotic manipulator; NONLINEAR-SYSTEMS; CONTROLLER; DESIGN;

D O I：

10.2316/J.2019.201-2931

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For robotic systems that do not have joint torques as direct inputs, the computed torque approach, often based on a general dynamic model that is linear with respect to joint velocities and accelerations, is very difficult to implement. We have applied a general model-free online reinforcement learning control methodology for discrete nonaffine nonlinear multiple-input-multiple-output systems to a second-order robotic system. The controller produced effective trajectory tracking in simulations of a two-degree-of-freedom robotic arm, and in actual experiments, with a three degree-of-freedom robotic arm.

引用

页码：136 / 143

页数：8

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