Abstract
Iterative learning control (ILC) is a method for reducing system tracking or estimation errors over multiple iterations by using information from past iterations. The disturbance observer (DOB) is used to estimate and mitigate disturbances within the system, while the system is being affected by them. ILC enhances system performance by introducing a feedforward signal in each iteration. However, its effectiveness may diminish if the conditions change during the iterations. On the other hand, although DOB effectively mitigates the effects of new disturbances, it cannot entirely eliminate them as it operates reactively. Therefore, neither ILC nor DOB alone can ensure sufficient robustness in challenging scenarios. This study focuses on the simultaneous utilization of ILC and DOB to enhance system robustness. The proposed methodology specifically targets dynamically different linearized systems performing repetitive tasks. The systems share similar forms but differ in dynamics (e.g. sizes, masses, and controllers). Consequently, the design of learning filters must account for these differences in dynamics. To validate the approach, the study establishes a theoretical framework for designing learning filters in conjunction with DOB. The validity of the framework is then confirmed through numerical studies and experimental tests conducted on unmanned aerial vehicles (UAVs). Although UAVs are nonlinear systems, the study employs a linearized controller as they operate in proximity to the hover condition. A video introduction of this paper is available via this link: this https URL.
Abstract (translated)
迭代学习控制（ILC）是一种通过利用过去的迭代信息来减少系统跟踪或估计误差的方法。扰动观测器（DOB）用于估计和减轻系统受到的影响。在每次迭代中引入前一次迭代的信息，从而增强系统的性能。然而，如果迭代过程中条件发生变化，其效果可能会减弱。另一方面，尽管DOB有效地减轻了新的干扰的影响，但它无法完全消除它们，因为它是以反应方式工作的。因此，ILC和DOB单独不能确保在具有挑战性的场景中具有足够的鲁棒性。 本研究关注的是同时使用ILC和DOB来增强系统的鲁棒性。所提出的方法针对执行重复任务的动态不同线性化系统。这些系统具有相似的形式，但动态（如大小，质量和控制器）有所不同。因此，设计学习滤波器时必须考虑这些动态差异。为了验证这种方法，研究建立了一个与DOB相结合的理论框架来设计学习滤波器。然后通过数值研究和无人飞行器（UAV）的实验测试来证实该框架的有效性。尽管UAV是 nonlinear 系统，但研究使用线性化控制器，因为它们在接近悬停状态时操作。本文的简介视频可以通过这个链接查看：https:// this URL。
URL
https://arxiv.org/abs/2404.10231
