面向四足机器人运动的融合数据驱动误差模型的自适应模型预测控制
Adaptive Model Predictive Control with Data-driven Error Model for Quadrupedal Locomotion
AI总结：
针对四足机器人MPC控制中降阶模型与真实机器人存在差距导致性能下降的问题，提出融合ARMAV数据驱动误差模型的自适应MPC控制器，通过误差补偿提升运动性能，经承载20kg未建模负载的大型四足机器人仿真与实机验证有效。
摘要：
模型预测控制（Model Predictive Control，MPC）的控制律高度依赖机器人模型。然而，存在不确定性的降阶控制模型与真实机器人之间始终存在差距，这会降低控制性能。为解决这一问题，我们提出了一种将数据驱动误差模型集成到传统MPC中的四足机器人控制器。我们的方法利用传感器采集的真实世界数据来弥补控制模型的缺陷。具体而言，我们采用自回归滑动平均向量（Autoregressive Moving Average Vector，ARMAV）模型，基于数据构建四足机器人的状态误差模型。随后，预测得到的状态误差被用于调整由MPC生成的未来机器人预测状态。通过这种方式，我们提出的控制器能够为系统提供更精确的输入，即使在模型参数不准确或存在干扰的情况下，也能使系统达到期望状态。该控制器具备部分消除模型与真实机器人之间差异的能力，从而提升四足机器人的运动性能。我们通过在一台大型四足机器人上开展的仿真与真实世界实验验证了所提方法，实验中机器人承载了20千克的未建模负载（占体重的84%）。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
