Achieving high-precision control for robotic systems is hindered by the low-fidelity dynamical model and external disturbances. Especially, the intricate coupling between internal uncertainties and external disturbances further exacerbates this challenge. This study introduces an effective and convergent algorithm enabling accurate estimation of the coupled disturbance via combining control and learning philosophies. Concretely, by resorting to Chebyshev series expansion, the coupled disturbance is firstly decomposed into an unknown parameter matrix and two known structures dependent on system state and external disturbance respectively. A regularized least squares algorithm is subsequently formalized to learn the parameter matrix using historical time-series data. Finally, a polynomial disturbance observer is specifically devised to achieve a high-precision estimation of the coupled disturbance by utilizing the learned portion. The proposed algorithm is evaluated through extensive simulations and real flight tests. We believe this work can offer a new pathway to integrate learning approaches into control frameworks for addressing longstanding challenges in robotic applications.

翻译：实现机器人系统的高精度控制受到低精度动力学模型和外部扰动的阻碍。特别是内部不确定性与外部扰动之间复杂的耦合关系进一步加剧了这一挑战。本研究提出了一种有效且收敛的算法，通过融合控制与学习思想实现对耦合扰动的高精度估计。具体而言，借助切比雪夫级数展开，首先将耦合扰动分解为一个未知参数矩阵和两个分别依赖于系统状态与外部扰动的已知结构。随后形式化一种正则化最小二乘算法，利用历史时序数据学习该参数矩阵。最后，专门设计了一种多项式扰动观测器，通过利用已学习部分实现对耦合扰动的高精度估计。所提算法通过大量仿真和真实飞行测试进行了验证。我们相信这项工作为将学习方法融入控制框架以解决机器人应用中长期存在的挑战提供了新途径。