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基于动力学模型的工业机器人导纳控制研究 被引量:9

Research on admittance control of industrial robot based on dynamics model
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摘要 提出了一种基于动力学模型的导纳控制算法,用来实现机器人末端力和位置的柔顺控制,可以在速度模式下控制机器人运动,以保证无外力接触时的轨迹跟踪精度.首先,根据牛顿-欧拉法建立机器人动力学模型;然后,通过粒子群算法辨识动力学模型参数,得到完整的动力学模型;在此基础上,计算机器人末端位置误差和外力,利用设计的导纳控制器实现机器人的柔顺控制,用Matlab的Simulink仿真模块验证了基于动力学模型导纳控制的有效性和可靠性.仿真结果表明:机器人末端没有与环境接触时,具有较高的跟踪精度;与环境接触时,机器人末端会产生位置误差和外力,从而实现机器人的柔顺控制. An admittance control algorithm based on dynamics model was proposed to achieve the compliance control of the end force and position of the robot.The robot motion can be controlled in the speed mode to ensure the trajectory tracking accuracy without external force contact by using this algorithm. Firstly, the robot dynamics model was established according to the Newton-Euler method.Then,in order to obtain a complete dynamic model,the particle swarm optimization was used to identify the dynamics model parameters.On these bases,the robot end position error and external force were calculated.By using the designed admittance control algorithm,the robot flexibility control was achieved.And the effectiveness and reliability of the admittance control based on dynamics model were verified by using the Simulink simulation module of Matlab.The simulation results show that the robot end has high tracking accuracy when it is not in contact with the environment;when it is in contact with the environment,the end of the robot generates position errors and external forces,so as to achieve the compliance control of the robot.
作者 叶伯生 陶婕妤 张文彬 李本旺 YE Bosheng;TAO Jieyu;ZHANG Wenbin;LI Benwang(School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China;Chongqing Dexin Robot Testing Center Co.Ltd.,Chongqing 400700,China)
出处 《华中科技大学学报(自然科学版)》 EI CAS CSCD 北大核心 2020年第8期98-102,108,共6页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 国家重点研发计划资助项目(2017YFB1301400) 国家自然科学基金资助项目(51475185)。
关键词 动力学模型 导纳控制 柔顺控制 牛顿-欧拉法 粒子群算法 dynamics model admittance control compliance control Newton-Euler method particle swarm optimization
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