用高斯过程估计机器人模型误差，实现无先验不确定性的精准轨迹跟踪。
A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
- 基于高斯过程回归估计未知模型偏差
- 在反馈线性化中引入自适应鲁棒项，确保轨迹渐近跟踪
- 适合动态模型不完整但需高精度控制的机械臂场景
本文提出一种新型学习型鲁棒反馈线性化策略，用于确保一类拉格朗日系统的精确轨迹跟踪。假设已知系统动力学的名义模型，但未提供模型失配的先验界。所提方法的核心是采用高斯过程回归（GPR）框架来估计模型失配，并将该估计量加入经典反馈线性化外环。为补偿残余不确定性，控制器进一步增强鲁棒性，其附加项的大小依据GPR提供的方差设计。理论上证明，在高概率下，该方案可保证对期望轨迹的渐近跟踪。数值实验在双自由度平面机械臂上验证了该策略的有效性。
原文摘要 · Abstract (English)
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback linearization scheme based on the nominal knowledge available. Then, to compensate for the residual uncertainty, we robustify the controller including an additional term whose size is designed based on the variance provided by the GPR framework. We proved that, with high probability, the proposed scheme is able to guarantee asymptotic tracking of a desired trajectory. We tested numerically our strategy on a 2 degrees of freedom planar robot.
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