用于鲁棒双足行走的非线性模型预测控制：探索角动量与质心高度变化
Nonlinear Model Predictive Control for Robust Bipedal Locomotion: Exploring Angular Momentum and CoM Height Changes
AI总结：
本研究提出一种具备步位调整、质心高度变化和角动量自适应能力的新型非线性模型预测控制框架，通过非线性倒立摆加飞轮模型构建二次约束二次规划问题并快速求解，生成鲁棒行走模式，经仿真验证了多场景双足行走适应性。
摘要：
人类能够利用多种平衡策略（例如步位调整和角动量自适应）在动态干扰下行走时保持平衡。本研究提出了一种新型非线性模型预测控制（NMPC）框架以实现鲁棒行走，该框架具备步位调整、质心（CoM）高度变化和角动量自适应能力。这些特性通过将零力矩点约束在支撑多边形内来实现。通过采用非线性倒立摆加飞轮模型，研究考虑了上半身转动和垂直高度运动的影响。由此，该NMPC被构建为一个二次约束二次规划问题，可通过序列二次规划快速求解。借助这一统一框架，系统能够基于状态估计生成利用反应性迈步、身体倾斜和质心高度变化的鲁棒行走模式。仿真研究已验证了该方法在多场景下双足行走的适应性。
英文摘要：
Human beings can utilize multiple balance strategies, e.g. step location adjustment and angular momentum adaptation, to maintain balance when walking under dynamic disturbances. In this work, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for robust locomotion, with the capabilities of step location adjustment, Center of Mass (CoM) height variation, and angular momentum adaptation. These features are realized by constraining the Zero Moment Point within the support polygon. By using the nonlinear inverted pendulum plus flywheel model, the effects of upper-body rotation and vertical height motion are considered. As a result, the NMPC is formulated as a quadratically constrained quadratic program problem, which is solved fast by sequential quadratic programming. Using this unified framework, robust walking patterns that exploit reactive stepping, body inclination, and CoM height variation are generated based on the state estimation. The adaptability for bipedal walking in multiple scenarios has been demonstrated through simulation studies.
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。
