控制理论(社会学)
倒立摆
仿人机器人
模型预测控制
非线性系统
二次规划
计算机科学
稳健性(进化)
凸优化
机器人运动
正多边形
机器人
数学
数学优化
移动机器人
机器人控制
人工智能
控制(管理)
生物化学
化学
物理
几何学
量子力学
基因
作者
Jiatao Ding,Linyan Han,Ligang Ge,Yizhang Liu,Jianxin Pang
出处
期刊:IEEE-ASME Transactions on Mechatronics
[Institute of Electrical and Electronics Engineers]
日期:2022-08-01
卷期号:27 (4): 2089-2097
被引量:4
标识
DOI:10.1109/tmech.2022.3173805
摘要
Robust locomotion is a challenging task for humanoid robots, especially when considering dynamic disturbances. This article proposes a disturbance observer-based cascaded model predictive control (MPC) approach for bipedal locomotion, with the capability of exploiting ankle, stepping, hip and height variation strategies. Specifically, based on the variable-height inverted pendulum model, a nonlinear MPC that is run at a low frequency is built for 3-D locomotion (i.e., with height variation) while accounting for the footstep modulation as well. Differing from previous works, the nonlinear MPC is formulated as a convex optimization problem by semidefinite relaxation. Subsequently, assuming a flywheel at the pelvis center, a linear MPC that is run at a high frequency is proposed to regulate angular momentum (e.g., through rotating the upper body), which is solved by convex quadratic programming. To run the cascaded MPC in a closed-loop manner, a high order sliding mode observer is designed to estimate system states and dynamic disturbances simultaneously. Simulation and hardware experiments demonstrate the walking robustness in real-world scenarios, including 3-D walking with varying speeds, walking across non-coplanar terrains and push recovery.
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