Hybrid‐Linear Inverted Pendulum Gait Planning With H ‐Infinity Uncertainty Compensation for Robust Biped Robot Walking

ABSTRACT Reduced‐order models (ROMs) are widely employed in biped robot control due to their computational efficiency, but their simplified representations often neglect critical nonlinear dynamics, leading to limited robustness under real‐world disturbances. To overcome this limitation, this paper introduces a robust hierarchical control framework that explicitly compensates for unmodelled dynamics and provides theoretical stability guarantees. The proposed architecture consists of two layers. At the high level, a hybrid‐linear inverted pendulum (HLIP) model generates real‐time gait commands, whereas an feedback law accounts for dynamic uncertainties introduced by model simplification. A Lyapunov‐based analysis is used to rigorously establish the stability of each planned foothold. At the low level, a whole‐body controller tracks both swing‐leg and centre‐of‐mass trajectories by solving a quadratic programme that maps task‐space accelerations to joint torques. The framework is validated in simulation and hardware experiments on the BRUCE platform. On flat ground, BRUCE maintains a steady walking speed of 0.3 m/s. When confronted with uneven terrain—simulated by randomly distributed 2.5‐cm planks as unmodelled disturbances—the robot preserves balance and velocity tracking. Comparative evaluations against the divergent component of motion (DCM) and reinforcement learning (RL)‐based methods demonstrate superior velocity tracking performance of the proposed approach, confirming its ability to reconcile the computational tractability of ROMs with the robustness missing in many traditional and learning‐based controllers.