基于带Guard Saltation Matrix的零阶随机非线性模型预测控制增强基于模型的运动鲁棒性
Robustifying Model-Based Locomotion by Zero-order Stochastic Nonlinear Model Predictive Control with Guard Saltation Matrix
AI总结：
针对足式运动接触不确定性问题，提出融合guard saltation矩阵与类扩展卡尔曼滤波协方差更新的零阶随机鲁棒NMPC方法，提升计算效率，经数值与轮足机器人硬件实验验证可行性与鲁棒性。
摘要：
本文提出一种随机/鲁棒非线性模型预测控制（NMPC）方法，以提升基于模型的足式运动在接触不确定性下的鲁棒性。我们利用guard saltation矩阵和类似扩展卡尔曼滤波的协方差更新，将接触不确定性整合到随机/鲁棒NMPC框架的协方差传播中。通过采用零阶算法，并在反馈增益的计算效率方面进行额外改进，我们实现了快速的随机/鲁棒NMPC计算。我们开展了数值实验，结果表明所提方法能够准确预测未来状态协方差，即使存在接触不确定性，也能生成满足约束的轨迹。我们还在轮足机器人的感知运动上进行了硬件实验，验证了所提方法在机载计算有限的真实系统中的可行性。
英文摘要：
This paper presents a stochastic/robust nonlinear model predictive control (NMPC) to enhance the robustness of model-based legged locomotion against contact uncertainties. We integrate the contact uncertainties into the covariance propagation of stochastic/robust NMPC framework by leveraging the guard saltation matrix and an extended Kalman filter-like covariance update. We achieve fast stochastic/robust NMPC computation by utilizing the zero-order algorithm with additional improvements in computational efficiency concerning the feedback gains. We conducted numerical experiments and demonstrate that the proposed method can accurately forecast future state covariance and generate trajectories that satisfies constraints even in the presence of the contact uncertainties. Hardware experiments on the perceptive locomotion of a wheeled-legged robot were also carried out, validating the feasibility of the proposed method in a real-world system with limited on-board computation.
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