基于缆索估计控制的线载欠驱动臂跃机器人：一种直接-间接自适应鲁棒组合方法
Cable Estimation-Based Control for Wire-Borne Underactuated Brachiating Robots: A Combined Direct-Indirect Adaptive Robust Approach
AI总结：
本文针对欠驱动臂跃机器人在柔性缆索上移动时动力学未知的问题，提出一种结合间接自适应估计与直接自适应增益更新的滑模鲁棒控制框架，并通过仿真验证了其在多种不确定性下的跟踪性能与系统辨识能力。
摘要：
本文提出了一种在线自适应鲁棒控制框架，用于在柔性缆索上移动的欠驱动臂跃机器人。由于柔性体的动力学模型在实际中未知，我们提出了一种间接自适应估计方案，将柔性缆索的未知动态效应近似为具有参数不确定性的外力。随后设计了一种基于边界层的滑模控制，以补偿残余未建模动态和时变扰动，其中控制增益通过辅助的直接自适应控制机制进行更新。通过Lyapunov方法进行了稳定性分析和自适应律推导，正式保证了机器人-缆索系统的稳定性和跟踪性能。仿真实验及与基线控制器的对比表明，所提出的直接-间接自适应鲁棒组合控制框架实现了可靠的跟踪性能和自适应系统辨识，使机器人能够在存在未建模动态、参数不确定性和非结构化扰动的情况下穿越柔性缆索。
英文摘要：
In this paper, we present an online adaptive robust control framework for underactuated brachiating robots traversing flexible cables. Since the dynamic model of a flexible body is unknown in practice, we propose an indirect adaptive estimation scheme to approximate the unknown dynamic effects of the flexible cable as an external force with parametric uncertainties. A boundary layer-based sliding mode control is then designed to compensate for the residual unmodeled dynamics and time-varying disturbances, in which the control gain is updated by an auxiliary direct adaptive control mechanism. Stability analysis and derivation of adaptation laws are carried out through a Lyapunov approach, which formally guarantees the stability and tracking performance of the robot-cable system. Simulation experiments and comparison with a baseline controller show that the combined direct-indirect adaptive robust control framework achieves reliable tracking performance and adaptive system identification, enabling the robot to traverse flexible cables in the presence of unmodeled dynamics, parametric uncertainties and unstructured disturbances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
