The robust balancing capability of humanoid robots against disturbances has
been considered as one of the crucial requirements for their practical mobility
in real-world environments. In particular, many studies have been devoted to
the efficient implementation of the three balance strategies, inspired by human
balance strategies involving ankle, hip, and stepping strategies, to endow
humanoid robots with human-level balancing capability. In this paper, a robust
balance control framework for humanoid robots is proposed. Firstly, a novel
Model Predictive Control (MPC) framework is proposed for Capture Point (CP)
tracking control, enabling the integration of ankle, hip, and stepping
strategies within a single framework. Additionally, a variable weighting method
is introduced that adjusts the weighting parameters of the Centroidal Angular
Momentum (CAM) damping control over the time horizon of MPC to improve the
balancing performance. Secondly, a hierarchical structure of the MPC and a
stepping controller was proposed, allowing for the step time optimization. The
robust balancing performance of the proposed method is validated through
extensive simulations and real robot experiments. Furthermore, a superior
balancing performance is demonstrated, particularly in the presence of
disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based
CP controller that employs the ankle, hip, and stepping strategies. The
supplementary video is available at https://youtu.be/CrD75UbYzdcComment: 19 pages,13 figure