  Model Predictive Control (MPC) relies heavily on the robot model for its
control law. However, a gap always exists between the reduced-order control
model with uncertainties and the real robot, which degrades its performance. To
address this issue, we propose the controller of integrating a data-driven
error model into traditional MPC for quadruped robots. Our approach leverages
real-world data from sensors to compensate for defects in the control model.
Specifically, we employ the Autoregressive Moving Average Vector (ARMAV) model
to construct the state error model of the quadruped robot using data. The
predicted state errors are then used to adjust the predicted future robot
states generated by MPC. By such an approach, our proposed controller can
provide more accurate inputs to the system, enabling it to achieve desired
states even in the presence of model parameter inaccuracies or disturbances.
The proposed controller exhibits the capability to partially eliminate the
disparity between the model and the real-world robot, thereby enhancing the
locomotion performance of quadruped robots. We validate our proposed method
through simulations and real-world experimental trials on a large-size
quadruped robot that involves carrying a 20 kg un-modeled payload (84% of body
weight).
