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).

翻译：模型预测控制（MPC）的控制律高度依赖于机器人模型。然而，简化的控制模型存在不确定性，其与真实机器人之间始终存在差距，这会降低控制性能。为解决此问题，我们提出了一种将数据驱动误差模型集成到传统MPC中的控制器，用于四足机器人控制。该方法利用传感器采集的真实世界数据来补偿控制模型的缺陷。具体而言，我们采用向量自回归滑动平均（ARMAV）模型，基于数据构建四足机器人的状态误差模型。预测的状态误差随后用于调整MPC生成的未来机器人状态预测值。通过这种方式，所提出的控制器能够为系统提供更精确的输入，使其即使在模型参数不准确或存在扰动的情况下也能达到期望状态。该控制器展现出部分消除模型与真实机器人之间差异的能力，从而提升了四足机器人的运动性能。我们通过仿真实验以及在大型四足机器人上进行的实物实验验证了所提方法的有效性，实验内容包括承载20千克未建模负载（占其体重的84%）。