Learning-Based Kinematic Control Using Position and Velocity Errors for Robot Trajectory Tracking
Learning-Based Kinematic Control Using Position and Velocity Errors for Robot Trajectory Tracking
复制标题
使用位置和速度误差进行机器人轨迹跟踪的基于学习的运动学控制
DOI：
10.1109/tsmc.2020.3013904
复制
发表时间：
2022-02-01
影响因子：
8.7
通讯作者：
Li, Hao
中科院分区：
文献类型：
--
作者：
Xu, Sheng;Ou, Yongsheng;Li, Hao
在本文中，我们使用演示学习（LFD）方法来解决轨迹跟踪问题。通过使用LFD方法，避免了跟踪控制器中的参数调整问题。因此，可以为参数调整经验有限的用户提供策略。运动跟踪问题被表述为二阶系统，目标是同时减少位置和速度的误差。控制器设计中应用了极限学习机（ELM）算法。速度和位置用作输入，输出是机器人校正的运动学运动。考虑到稳定性约束，控制器参数是从所需的人工或编程演示中学习的。在这项工作中，我们详细分析了系统的局部和全局渐近稳定性。通过仿真比较和使用 KUKA 机器人操纵器的实际实验证明了所提出策略的有效性。
In this article, we address the trajectory tracking problem using the learning from demonstration (LFD) method. By using the LFD method, the parameter adjusting problem in the tracking controller is avoided. Consequently, a strategy can be provided to users with limited parameter adjusting experience. The kinematic tracking problem is formulated as a second-order system and the objective is to simultaneously reduce the errors in position and velocity. The extreme learning machines (ELM) algorithm is applied in the controller design. The velocity and position are utilized as the inputs and the output is the robot corrected kinematic movement. The controller parameters are learned from the desired human or programming demonstrations taking into consideration the stability constraints. In this work, we analyze the system local and global asymptotic stability in detail. The effectiveness of the proposed strategy is demonstrated by simulation comparisons and a practical experiment using a KUKA robot manipulator.