Attitude control in the Mini Cheetah robot via MPC and reward-based feed-forward controller

摘要

In this paper, a model-free approach, based on a generalization of unsupervised Self Organizing Feature Maps, is introduced to compensate for attitude control errors in a simulated mini cheetah quadruped platform. Traditional techniques mainly exploit the potentialities of convex model predictive control (MPC) to efficiently regulate the robot attitude while moving in unstructured environments. However, they are based on the knowledge of the analytical model of the robot. If the robot structure undergoes significant modifications due, for example to an unbalancing of the robot weight caused by a load charged of the robot center of mass, the added value of a model-free, adaptive unsupervised nonlinear controller, acting as a feed-forward error compensator, shows its real effect when integrated with the model-based approach. Moreover, the proposed solution, acting as a self-learning feedforward controller, contributes to the control action only when needed, preserving the basic performance of the ongoing MPC action. The design of the control scheme and simulation results will be reported, showing the impact of the introduced model-free compensation on the quadruped robot locomotion.