Fault tolerant control method of manipulator based on deep reinforcement learning

LI Minghao; ZHANG Hua; LIU Manlu; LI Xinmao; ZHOU Qijie

doi:10.13873/J.1000-9787(2020)01-0053-03

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Fault tolerant control method of manipulator based on deep reinforcement learning

更新时间：2025-09-11

- Fault tolerant control method of manipulator based on deep reinforcement learning
- Issue 1, Pages: 53-55+59(2020)
- 作者机构：
  
  1. 西南科技大学信息工程学院
  2. 中国科学技术大学信息科学技术学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)01-0053-03
  CLC： TP241
- Published：2020
- 稿件说明：
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LI Minghao, ZHANG Hua, LIU Manlu, et al. Fault tolerant control method of manipulator based on deep reinforcement learning[J]. 2020, (1): 53-55+59.
DOI：

LI Minghao, ZHANG Hua, LIU Manlu, et al. Fault tolerant control method of manipulator based on deep reinforcement learning[J]. 2020, (1): 53-55+59. DOI： 10.13873/J.1000-9787(2020)01-0053-03.

摘要

针对机械臂突发单关节故障的情况

提出一种基于深度强化学习的机械臂容错控制方法。在建立环境模型和奖罚机制的基础上

针对机械臂正常运行和故障运行的情况

使用无模型的强化学习算法进行离线训练。在Rviz中建立机械臂模型并使用上述网络进行在线控制。实验证明:该算法可以有效完成机械臂的正常控制和容错控制。

Abstract

Aiming at the case of sudden single joint failure of the manipulator

a fault-tolerant control method based on deep reinforcement learning is proposed. On the basis of setting up environment model and rewardpenalty mechanism

model-free reinforcement learning algorithm is used to train offline in the case of normal operation and failure operation of the manipulator. Mechanical arm model is built in Rviz and online controlled is carried out by using the above network. Experiments show that the algorithm can effectively complete the normal control and fault tolerant control of the manipulator.

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