Force Control of a 1-DoF Cable Robot Using Deep Reinforcement Learning

被引：0

作者：

Hamann, Marcus ^{[1
]}

Brandl, Julius ^{[1
]}

Ament, Christoph ^{[1
]}

机构：

[1] Univ Augsburg, Fac Appl Comp Sci, Augsburg, Germany

来源：

2023 SICE INTERNATIONAL SYMPOSIUM ON CONTROL SYSTEMS, SICE ISCS | 2023年

关键词：

Force Control; Reinforcement Learning; DDPG; Cable Robot;

D O I：

10.23919/SICEISCS57194.2023.10079206

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 presents the force control of a single module of a cable robot based on a Deep Reinforcement Learning approach. This can be used for an underlying force control of a cable robot. Parameter studies of the actor and critic networks and the reward function are also presented. The controller is tested in a simulation as well as on the physical system. For the experiments on the physical system, the controller is compared to an expert tuned PID controller.

引用

页码：86 / 92

页数：7

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