期刊文献+

基于DSP的机器人末端力控制系统设计 被引量:3

Design of End Force Control System of Robot Based on DSP
在线阅读 下载PDF
导出
摘要 工业机器人的运动分为自由运动和受力约束运动两种不同的运动类型;受力约束运动不仅要进行精确的位置控制,而且要对接触力进行精确控制;文章对机械臂的末端力控制系统进行了研究;采用了高精度电动滑台作为力控制系统的执行机构,在电动滑台末端增加了柔性单元,从而克服了系统柔性不足的缺点,并且依据Lyapunov稳定性理论确定系统的二阶参数,使用Narendra提出的稳定自适应设计方法构建了电动滑台的数学模型;对机械臂末端柔顺系统的系统设计及算法进行了研究,消除了外界噪声对系统的干扰,解决了力控制的基本控制策略问题,优化了力控制时系统的响应特性。 The industrial robot motion can be divided into two different motion types of free motion and force-constrained motion.Not only the position of the force-constrained motion is controlled accurately,but also the contact force is controlled accurately.The paper studies the control system of end force of mechanical arm.High-precision electric sliding table is adopted as the actuator of the control system,and the flexible unit is added at the end of the electric sliding table,so that the drawback of system flexibility deficiency is overcome.The second order parameter of the system is determined based on the Lyapunov stability theory,and the stable adaptive design method proposed by Narendra is used to build the mathematical model of electric sliding table.The paper studies the design and algorithm of the soft system of the mechanical arm end.The influence of outside noise on the system is eliminated,the basic control strategy problem of force control is solved,and the response characteristics of the system is optimized when the system is controlled by force.
出处 《计算机测量与控制》 2017年第8期46-49,共4页 Computer Measurement &Control
基金 安徽省科技攻关项目(1604a0902125) 安徽省自然科学基金项目(1608085QF154) 芜湖市科技计划项目(2016cxy03)
关键词 工业机械臂 阻抗控制 受力约束运动 industrial mechanical arm impedance control force-constrained motion
  • 相关文献

参考文献3

二级参考文献21

  • 1潘泉,孟晋丽,张磊,程咏梅,张洪才.小波滤波方法及应用[J].电子与信息学报,2007,29(1):236-242. 被引量:119
  • 2张英会.弹簧手册[M].北京:机械工业出版社,2008.
  • 3孟晋丽,潘泉,张洪才.基于相邻尺度积系数的半软阈值小波滤波[J].电子与信息学报,2007,29(7):1649-1652. 被引量:12
  • 4GB/T 1239.6-92,圆柱螺旋弹簧设计计算[S]
  • 5Mallat S. A theory for multiresolution signal decomposition:the wavelet representation [J]. IEEE Transactions on PatternAnalysis and Machine Intelligence, 1989, 11(7): 674-693.
  • 6Donoho D L. De-noising by soft-thresholding[J]. IEEETransactions on Information Theory, 1995, 41(3): 613-627.
  • 7Zhang X P and Desai M D. Adaptive denoising based onSURE risk[J]. IEEE Signal Processing Letters, 1998, 5(10):265-267.
  • 8Donoho D L and Johnstone I M. Ideal spatial adaptation bywavelet shrinkage [J]. Biometriaka, 1994, 81(3): 425-455.
  • 9Krim H, Dewey T, Mallat S, et al. On denoising and bestsignal representation[J]. IEEE Transactions on InformationTheory, 1999, 45(7): 2225-2238.
  • 10Pan Q, Zhang L, Dai G Z, et al. Two denoising methods bywavelet transform[J]. IEEE Transactions on SignalProcessing. 1999, 47(12): 3401-3406.

共引文献156

同被引文献11

引证文献3

二级引证文献9

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部