双足步行的迭代学习控制

Qizhi Zhang, Chew Chee-Meng, Yali Zhou, Q. Zhao, Pei Li
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引用次数: 7

摘要

本文提出了一种迭代学习控制(ILC)方法用于两足行走控制。这款双足机器人的动力来自于脚后跟撞击前的脉冲推力。基于庞卡罗映射设计了ILC律,并将其应用于系统存在不确定性时每一步的期望脉冲推力的学习。仿真结果表明,在足部质量相对于骨盆质量不容忽视的情况下,所提出的双足步行控制方法能够有效地跟踪所需的步长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Iterative learning control for biped walking
In this paper, an iterative learning control (ILC) approach is proposed for biped walking control. The biped robot is powered by applying an impulsive push along the stance leg just before the heel strikes. The ILC law is designed based on Poincaré map, and applied to learn the desired impulsive push at every step in the presence of system uncertainties. The convergence of the proposed ILC approach for biped walking control is proofed, and the simulation results show that the proposed ILC approach for biped walking control can track the desired step length effectively, even if the mass of foot can not be ignored compared to that of pelvis.
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