摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了机器人S-机器人和自动化的新数据。根据NewsRx记者在Texas大学站的新闻报道，研究表明，“迭代学习控制(ILC)是一种通过使用过去迭代的信息来减少多个ITER上系统跟踪或估计误差的方法。当系统受到干扰时，干扰观测器(DOB)用于估计和减轻系统内的干扰。”这项研究的财政支持来自国家科学基金会(NSF)。新闻记者从德州农工大学的研究中获得了一句话：“ILC通过在每次迭代中引入前馈符号AL来提高系统性能。然而，如果迭代过程中条件发生变化，它的有效性可能会降低。另一方面，尽管DOB有效地缓解了新扰动的影响，但由于它的反应速率，它不能完全消除它们。因此，本文研究了ILC和DOB同时应用来提高系统鲁棒性的方法,该方法特别针对不同线性系统进行重复T要求,系统形式相似,但动态(如尺寸、质量和控制器)不同,因此,本文提出了一种基于ILC和DOB的方法。为了验证该方法的有效性,本文建立了一个结合DOB设计学习滤波器的理论框架,并通过对(UAVs)无人机的数值研究和实验测试验证了该框架的有效性。该研究采用了线性化控制器，因为它们在接近悬停条件的情况下运行。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Robotic s - Robotics and Automation. According to news reporting from College Station, T exas, by NewsRx journalists, research stated, “Iterative learning control (ILC) is a method for reducing system tracking or estimation errors over multiple iter ations by using information from past iterations. The disturbance observer (DOB) is used to estimate and mitigate disturbances within the system, while the syst em is being affected by them.” Financial support for this research came from National Science Foundation (NSF). The news correspondents obtained a quote from the research from Texas A& M University, “ILC enhances system performance by introducing a feedforward sign al in each iteration. However, its effectiveness may diminish if the conditions change during the iterations. On the other hand, although DOB effectively mitiga tes the effects of new disturbances, it cannot entirely eliminate them as it ope rates reactively. Therefore, neither ILC nor DOB alone can ensure sufficient rob ustness in challenging scenarios. This study focuses on the simultaneous utiliza tion of ILC and DOB to enhance system robustness. The proposed methodology speci fically targets dynamically different linearized systems performing repetitive t asks. The systems share similar forms but differ in dynamics (e.g. sizes, masses , and controllers). Consequently, the design of learning filters must account fo r 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 e xperimental tests conducted on unmanned aerial vehicles (UAVs). Although UAVs ar e nonlinear systems, the study employs a linearized controller as they operate i n proximity to the hover condition.”
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