Abstract
In this paper, an adaptive controller is designed for the synchronization of the trajectory of a robot with unknown kinematics and dynamics to that of the current human trajectory in the task space using the delayed human trajectory information. The communication time delay may be a result of various factors that arise in human-robot collaboration tasks, such as sensor processing or fusion to estimate trajectory/intent, network delays, or computational limitations. The developed adaptive controller uses Barrier Lyapunov Function (BLF) to constrain the Cartesian coordinates of the robot to ensure safety, an ICL-based adaptive law to account for the unknown kinematics, and a gradient-based adaptive law to estimate unknown dynamics. Barrier Lyapunov-Krasovskii (LK) functionals are used for the stability analysis to show that the synchronization and parameter estimation errors remain semi-globally uniformly ultimately bounded (SGUUB). The simulation results based on a human-robot synchronization scenario with time delay are provided to demonstrate the effectiveness of the designed synchronization controller with safety constraints.
Abstract (translated)
在这篇论文中，设计了一种自适应控制器，用于在任务空间中使用延迟的人体轨迹信息同步机器人（其动力学和运动学未知）的轨迹与当前人体的轨迹。通信时延可能是由人类-机器人协作任务中的各种因素引起的，例如传感器处理或融合以估计轨迹/意图、网络延迟或计算限制等。 开发的自适应控制器利用障碍Lyapunov函数（BLF）来约束机器人的笛卡尔坐标，确保其安全性；基于迭代学习控制（ICL）的自适应律用于应对未知运动学问题；以及基于梯度的自适应律用于估计未知的动力学特性。使用Barrier Lyapunov-Krasovskii (LK)泛函进行稳定性分析，以表明同步误差和参数估计误差在半全局一致最终有界（SGUUB）范围内。 论文提供了具有时间延迟的人机同步场景下的仿真结果，展示了所设计的安全约束同步控制器的有效性。
URL
https://arxiv.org/abs/2509.22976
