Iterative machine learning for precision trajectory tracking with series elastic actuators
Iterative machine learning for precision trajectory tracking with series elastic actuators
复制标题
使用串联弹性执行器进行精确轨迹跟踪的迭代机器学习
DOI：
10.1109/amc.2019.8371094
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
S. Devasia
中科院分区：
文献类型：
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作者：
N. Banka;W. T. Piaskowy;J. Garbini;S. Devasia
当机器人在未知环境中操作时，位置上的微小误差可能会导致接触力的巨大变化，特别是在典型的高阻抗设计中。这可能会损害周围环境和/或机器人。串联弹性致动器(SEA)是一种常用的降低机械臂输出阻抗的方法，以提高对施加在环境中的力的控制权威。然而，增加对低阻抗力的控制是以较低的定位精度和带宽为代价的。本文研究了在使用SEA时如何使用迭代学习的前馈命令来改进位置跟踪。在每次迭代中，系统对量化输入的输出响应被用来估计线性化的局部系统模型。这些估计模型是使用复值高斯过程回归(CGPR)技术获得的，然后用于根据前一次迭代的误差生成新的前馈输入命令。本文以两自由度机械臂为例说明了这种迭代机器学习(IML)技术，并演示了IML方法的成功收敛以减小跟踪误差。
When robots operate in unknown environments small errors in positions can lead to large variations in the contact forces, especially with typical high-impedance designs. This can potentially damage the surroundings and/or the robot. Series elastic actuators (SEAs) are a popular way to reduce the output impedance of a robotic arm to improve control authority over the force exerted on the environment. However this increased control over forces with lower impedance comes at the cost of lower positioning precision and bandwidth. This article examines the use of an iteratively-learned feedforward command to improve position tracking when using SEAs. Over each iteration, the output responses of the system to the quantized inputs are used to estimate a linearized local system models. These estimated models are obtained using a complex-valued Gaussian Process Regression (cGPR) technique and then, used to generate a new feedforward input command based on the previous iteration's error. This article illustrates this iterative machine learning (IML) technique for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful convergence of the IML approach to reduce the tracking error.