Model Predictive Optimization and Control of Quadruped Whole-Body Locomotion

In this paper, a framework of model predictive optimization and control for quadruped whole-body locomotion is presented, which enables dynamic balance and minimizes the control effort. First, we propose a hierarchical control scheme consisting of two modules. The first layer is to find an optimal ground reaction force (GRF) by employing inner model predictive control (MPC) along a full motor gait cycle, ensuring the minimal energy consumption of the system. Based on the output GRF of inner layer, the second layer is designed to prioritize tasks for motor execution sequentially using an outer model predictive control. In inner MPC, an objective function about GRF is designed by using a model with relatively long time horizons. Then a neural network solver is used to obtain the optimal GRF by minimizing the objective function. By using a two-layered MPC architecture, we design a hybrid motion/force controller to handle the impedance of leg joints and robotic uncertainties including external perturbation. Finally, we perform extensive experiments with a quadruped robot, including the crawl and trotting gaits, to verify the proposed control framework.