Robust Hierarchical Locomotion Control for Quadruped Robots on Lunar Surfaces
Abstract
This paper presents a robust hierarchical motion control framework for quadruped robots, emphasizing feasible motion planning and disturbance rejection rather than explicit disturbance identification. The proposed model predictive control-sliding mode control (MPC-SMC) combines model predictive control (MPC) with sliding mode control (SMC) techniques to improve tracking performance under challenging terrains.Firstly, a complete dynamic model of the quadruped is established. Secondly, a hierarchical control strategy is designed to ensure stable locomotion and suppress disturbances. Finally, the framework is verified through numerical simulations. Simulation results show that the proposed method significantly enhances tracking accuracy. For 10 mm obstacles, the overall spatial root mean square error (RMSE) is reduced from about 26 mm to 8 mm, and for 200 mm obstacles, from around 46 mm to 26 mm, demonstrating consistent improvement across different terrain difficulties compared to MPC with proportional-integral-derivative (MPC-PID) control. These results indicate that the proposed framework provides a reliable solution for quadruped locomotion in complex environments and contributes to the development of robust motion control strategies for legged robots.