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In this paper, we propose a novel framework on force-and-moment-based ModelPredictive Control (MPC) for dynamic legged robots. Specifically, we present aformulation of MPC designed for 10 degree-of-freedom (DoF) bipedal robots usingsimplified rigid body dynamics with input forces and moments. This MPCcontroller will calculate the optimal inputs applied to the robot, including3-D forces and 2-D moments at each foot. These desired inputs will then begenerated by mapping these forces and moments to motor torques of 5 actuatorson each leg. We evaluate our proposed control design on physical simulation ofa 10 degree-of-freedom (DoF) bipedal robot. The robot can achieve fast walkingspeed up to 1.6 m/s on rough terrain, with accurate velocity tracking. With thesame control framework, our proposed approach can achieve a wide range ofdynamic motions including walking, hopping, and running using the same set ofcontrol parameters.

Force-and-moment-based Model Predictive Control for Achieving Highly Dynamic Locomotion on Bipedal Robots