Quadruped Robot Motion Control Method Based on Adaptive Weight Coefficients MPC

In real-world environments, quadruped robots often need to traverse unstructured terrains and may encounter various unexpected impacts. To enhance the motion balance performance of quadruped robots when facing unknown disturbances in such environments, we propose a fuzzy adaptive weight controller based on Model Predictive Control (MPC). By conducting an in-depth analysis of the impact of weight coefficients in the MPC's objective function on control effectiveness, we have designed an adaptive fuzzy algorithm. This algorithm dynamically adjusts the weight coefficients according to the current errors in the robot's roll and pitch angles, as well as their rates of change. Subsequently, the MPC controller calculates the optimal control torques using these adjusted weight coefficients. To validate the effectiveness of this strategy, we conducted simulations of lateral impacts and walking on unstructured terrains in Gazebo. The simulation results demonstrate that, under various test conditions, the proposed adaptive weight MPC controller significantly improves the robot's motion stability, showcasing its strong capability to handle unknown disturbances.