Joint stick-slip friction compensation for robotic manipulators by iterative learning
Joint stick-slip friction compensation for robotic manipulators by iterative learning
复制标题
通过迭代学习补偿机器人机械臂的关节粘滑摩擦
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
Jing
中科院分区：
文献类型：
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作者：
Jing
研究了机器人期望轨迹跟踪中内部关节粘滑摩擦效应的补偿问题。一个PD型迭代学习控制，其中包括一个稳定的反馈控制机器人动力学，应用到补偿的摩擦。两连杆机器人的仿真结果表明，我们的摩擦补偿方案是有效的不同的摩擦模型，其特性是不确切的先验。<<ETX>>
This paper studies the compensation of internal joint stick-slip friction effects for desired trajectory tracking of robotic manipulators. A PD type iterative learning control, which incorporates a stabilizing feedback control for robot dynamics, is applied to compensate for the friction. Simulations of a two-link robotic manipulator show that our friction compensation scheme is effective for different friction models whose characteristics are not exactly known a priori.<<ETX>>