Learning control scheme for a class of robot systems with elasticity
Learning control scheme for a class of robot systems with elasticity
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一类具有弹性的机器人系统的学习控制方案
DOI：
10.1109/cdc.1986.267157
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发表时间：
1986
期刊：
影响因子：
--
通讯作者：
S. Arimoto
中科院分区：
文献类型：
--
作者：
F. Miyazaki;S. Kawamura;M. Matsumori;S. Arimoto
针对由刚性连杆组成、弹性传动驱动的机器人，提出了一种新型的迭代学习控制方案。该方法基于机械机器人操作的“改进过程”的学习方案，在某种意义上说，它通过使用前一次操作的数据来改进机器人的下一次操作。在第一阶段中，采用刚性连杆子系统的改进过程，以便通过将与致动器的其他驱动子系统的互连项作为假设输入来实现期望的运动模式。在第二阶段，另一个改进过程是用来使假设的输入方面的控制力矩的执行器。在某些合理的条件下，保证了这种“两阶段改进过程”收敛到期望的运动轨迹。为了表明所提出的学习计划的有效性，一些部分结果的实际应用，这种方法的实际机器人操作器，连同计算机模拟的数值结果，给出。
A new type of iterative learning control scheme is proposed for robot manipulators composed of rigid links and driven by actuators through transmissions with elasticity. This method is based on the learning scheme called "betterment process" for operation of a mechanical robot in a sense that it betters the next operation of a robot by using previous operation's data. In the first stage, a betterment process for the rigid link subsystem is employed in order to realize a desired motion pattern by regarding the interconnected terms with the other drive subsystem of actuators as hypothetical inputs. In the second stage, another betterment process is employed to make the hypothetical inputs in terms of the control torques for actuators. The convergence of this "2-stage betterment process" to the desired motion trajectory is assured under some reasonable conditions. To show the effectiveness of the proposed learning scheme, some partial results on practical applications of this method for an actual robot manipulator, together with numerical results by computer simulation, are given.