Nonlinear model predictive control for robust bipedal locomotion: exploring angular momentum and CoM height changes

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 gait pattern generation, 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 employing 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. Consequently, robust walking patterns that exploit reactive stepping, body inclination, and CoM height variation are generated. Adaptable walking in multiple scenarios has been demonstrated through simulation studies.