High-Frequency Control for Perturbation Rejection in Dynamic Biped Walking Via Sequential Centroidal Model Predictive Control

The robust rejection of unforeseen external perturbations by bipedal robots during walking poses a significant challenge. This article presents a sequential centroidal model predictive control (SC-MPC) framework based on foothold and contact wrench decomposition, enabling bipedal robots to robustly reject external perturbations during dynamic walking. Unlike methods that simultaneously optimize footholds and contact wrenches, which lead to nonlinearity and computational complexity, we decouple this complex problem into two sequential lightweight MPC problems to ensure an efficient online solution. The SC-MPC framework first employs a low-fidelity linear inverted pendulum model to predict the reactive foothold sequence, incorporating velocity tracking, kinematic reachability, and slack constraints for heuristic foothold predictions. Subsequently, using the optimized footholds, a high-fidelity variable-inertia centroidal dynamics model is used to predict the continuously varying contact wrenches, with closed-form contact stability constraints. The proposed SC-MPC method enhances the prediction frequency of multistep planning from typically below 100 Hz to at least 200 Hz, enabling the robot to respond more quickly to unknown disturbances. With the torque-controlled bipedal robot BHR8TC, the proposed method is validated through extensive simulations and experiments involving external force and terrain perturbations. Comparative results with different control methods demonstrate the superior performance of SC-MPC in perturbation rejection.