Abstract: The dynamic balancing capability of humanoid robots in unstructured terrain and under external force disturbances constitutes a critical challenge for their practical application. This paper proposes an integrated framework combining disturbance estimation, online motion planning, and whole-body control to enhance the robustness of humanoid robots against external disturbances and their adaptability to complex terrains. Firstly, a disturbance observer based on the velocity residual of the Divergent Component of Motion (DCM) is designed. It enables real-time estimation of external force disturbances acting on the robot’s Center of Mass (CoM) without requiring foot force sensors and demonstrates enhanced robustness against model errors and sensor noise. Next, a hierarchical online locomotion planner is proposed: a footstep planner optimizes foot placement positions and gait cycles in real-time using Quadratic Programming (QP), while a gait generator employs a 3D DCM-based Model Predictive Controller (MPC) to generate robust CoM trajectory and gaits based on the planned foot placements. Then, a Disturbance Rejection Whole-Body Controller (DR-WBC) is developed, which incorporates the estimated disturbances into the whole-body dynamics constraints, achieving active disturbance compensation and direct joint torque control. Finally, simulations and experiments conducted on the humanoid robot Dexbot from Harbin Institute of Technology (HIT) demonstrate that the proposed framework effectively enhances the robot’s stable walking capability under disturbances and on uneven terrain, validating its effectiveness and engineering practicality.

Key words: Humanoid robot control')">Humanoid robot control, Disturbance rejection, Model predictive control, Divergent component of motion, Whole-body control