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基于对决深度Q网络的机器人自适应PID恒力跟踪研究

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为确保机器人与环境接触时能保持稳定的接触力,基于对决深度Q网络设计一种自适应PID控制恒力跟踪算法.分析机器人与外界的接触过程,并构建基于PID算法的机器人力控制器;提出基于对决深度Q网络的自适应PID算法,以适应外界环境的变化,该算法利用对决深度Q网络自主学习、寻找最优的控制参数;最后,通过Coopeliasim与MATLAB软件平台展开机器人恒力跟踪实验.仿真结果表明:提出的基于对决深度Q网络的自适应PID算法能够获得较好的力跟踪效果,验证了算法的可行性;相比于深度Q网络算法,力误差绝对值的平均值减少了 51.6%,且收敛速度得到提升,使机器人能够更好地跟踪外界环境.
Research on Robot Constant Force Tracking Based on an Adaptive PID Algorithm with Dueling Q network
To ensure that a robot can maintain a stable contact force when it contacts the environment,an adaptive PID control for robot constant force tracking was designed based on a dueling deep Q-network.The contact process between a robot and the environment was analyzed,and a robot force controller based on a PID algorithm was constructed.The adaptive PID algorithm based on a dueling deep Q-network was proposed to adapt to changes in the external environment.In this algorithm,the dueling deep Q network was used to learn and find optimal control parameters.Finally,robot constant force tracking experiments were expanded on Coopeliasim and MAT-LAB software platforms.The simulation results show that the adaptive PID algorithm based on the dueling deep Q-network can achieve good force-tracking effects,verifying the algorithm's feasibility;compared with a deep Q network algorithm,the average absolute value of the force error is reduced by 51.6%,and the convergence speed is improved,allowing the robot to track the external environment better.

robotcontact force controladaptive PID controldueling deep Q-network

杜亮、梅雪川

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广东轻工职业技术大学机电技术学院,广东广州 510300

国机智能科技有限公司,广东广州 510700

机器人 恒力控制 自适应PID控制 对决深度Q网络

广州市科技计划项目国家市场监管重点实验室(智能机器人安全)开放课题广东省教育厅特色创新项目

202002030243GQI-KFKT2023042021KTSCX203

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(15)