A Distributed Model Predictive Control approach for robust postural stability of a humanoid robot

A novel formulation of the synthesis of motor coordination for humanoid whole-body motion is proposed in this paper, in order to ensure robust control of postural stability. It relies on the distributed model predictive control framework to coordinate, in an optimal way, several objectives. The effectiveness of this control technique to maintain postural stability of a biped against strong external disturbances is shown. Control of the horizontal dynamics of the center of mass can withstand limited perturbations. Thus postural stability criteria are specified with respect to the robot center of mass vertical and horizontal dynamics, and to the angular dynamics of its torso. Formulating the balance problem in a predictive form and distributing at different time scales significantly increases the robustness of the system to external disturbances, in terms of both tip-over and slippage risks. This original control architecture is validated through the simulation of an iCub robot performing a walking activity under unknown external actions.