Research on Environmental Adaptability of Force–Position Hybrid Control for Quadruped Robots Based on Model Predictive Control

This study proposes a force–position hybrid control method for quadruped robots based on the Model Predictive Control (MPC) algorithm, aiming to address the challenges of stability and adaptability in complex terrain environments. Traditional control methods for quadruped robots are often based on simplified models, neglecting the impact of complex terrains and unstructured environments on control performance. To enhance the real-world performance of quadruped robots, this paper employs the MPC algorithm to integrate force and position control to achieve precise force–position hybrid regulation. By transforming foot-end forces into joint torques and optimizing them using kinematic inverse solutions, the robot’s stability and motion accuracy in challenging terrains is further enhanced. In this study, a Kalman filter-based state estimation method is adopted to estimate the robot’s state in real time, enabling closed-loop control through the MPC framework, combined with kinematic inverse solutions for hybrid control. The experimental results demonstrate that the proposed MPC algorithm significantly improves the robot’s adaptability and control accuracy across various terrains. In particular, it exhibits superior performance and robustness in multi-contact and uneven terrain scenarios. This research provides a novel approach for deploying quadruped robots in dynamic and complex environments and offers strong support for further optimization of motion control strategies.