  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.
