四足机器人负载运输的间接自适应律预测控制
Predictive Control with Indirect Adaptive Laws for Payload Transportation by Quadrupedal Robots
AI总结：
本文提出一种基于间接自适应律的预测控制框架，用于四足机器人在复杂地形中稳健运输未知负载。
摘要：
本文正式开发了一种新颖的分层规划与控制框架，用于四足机器人稳健的负载运输，整合了模型预测控制（MPC）算法与基于梯度下降的自适应更新律。在框架的高层，间接自适应律在变化负载下估计减少阶（模板）运动模型的未知参数。这些估计参数被输入MPC算法进行实时轨迹规划，其中包含一个凸稳定性准则，以确保模板模型估计误差的稳定性。由高层自适应MPC（AMPC）生成的最优减少阶轨迹随后传递给低层非线性全身体控制器（WBC）进行跟踪。广泛的数值研究验证了该框架的能力，展示了机器人在平坦地形上运输未建模、未知静态负载的能力，实验中达到109%，在粗糙实验地形上达到91%。机器人还成功管理动态负载，在粗糙地形上其质量的73%。与正常MPC和L1 MPC的性能比较表明有显著改进。此外，全面的硬件实验在室内外环境进行，证实了该方法在存在负载变化、推扰和障碍物等不确定性的情况下在粗糙地形上的有效性。
英文摘要：
This paper formally develops a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots, integrating a model predictive control (MPC) algorithm with a gradient-descent-based adaptive updating law. At the framework's high level, an indirect adaptive law estimates the unknown parameters of the reduced-order (template) locomotion model under varying payloads. These estimated parameters feed into an MPC algorithm for real-time trajectory planning, incorporating a convex stability criterion within the MPC constraints to ensure the stability of the template model's estimation error. The optimal reduced-order trajectories generated by the high-level adaptive MPC (AMPC) are then passed to a low-level nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations validate the framework's capabilities, showcasing the robot's proficiency in transporting unmodeled, unknown static payloads up to 109% in experiments on flat terrains and 91% on rough experimental terrains. The robot also successfully manages dynamic payloads with 73% of its mass on rough terrains. Performance comparisons with a normal MPC and an L1 MPC indicate a significant improvement. Furthermore, comprehensive hardware experiments conducted in indoor and outdoor environments confirm the method's efficacy on rough terrains despite uncertainties such as payload variations, push disturbances, and obstacles.
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