Learning control for robot tasks under geometric endpoint constraints
Learning control for robot tasks under geometric endpoint constraints
复制标题
几何端点约束下机器人任务的学习控制
DOI：
10.1109/robot.1992.219949
复制
发表时间：
1992
期刊：
影响因子：
--
通讯作者：
T. Naniwa
中科院分区：
文献类型：
--
作者：
S. Arimoto;T. Naniwa
针对一类具有几何端点约束的机器人任务，提出了一种基于训练的学习控制理论。提出了一种更新控制输入的算法，使得下一个输入由前一个输入加上前一个受曲面约束的机器人端点处的速度和力误差修正项组成。仿真结果表明，在给定曲面上的力的情况下，位置和力跟踪收敛到期望的路径。结果表明，即使在几何约束的情况下，机器人动力学也满足关节力矩输入向量相对于关节速度向量的无源性条件。给出了位置误差和力误差收敛的理论证明。在证明过程中，机械臂误差动力学的无源性这一宽松概念起着至关重要的作用。
A theory of training-based learning control is developed for a class of robotic tasks under geometric endpoint constraints. An algorithm for updating the control input which makes the next input consist of the previous input plus modified terms of previous velocity and force errors at the robot endpoint constrained on a surface is proposed. Simulation results are presented to demonstrate the convergence of position and force tracking to a desired path with force specified on the surface. It is shown that the robot dynamics satisfies the passivity condition regarding the joint torque input vector versus the joint velocity vector, even in the case of geometric constraints. A theoretical proof of the convergence of position and force errors is given. In the proof, a relaxed concept of passivity of error dynamics of robot arms plays a crucial role.<<ETX>>