权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Data-based Iterative Control using Complex-Kernel Regression for Precision SEA Robots

使用复杂核回归进行基于数据的迭代控制用于精密 SEA 机器人

基本信息

批准号：
1824660
负责人：
Santosh Devasia
金额：
$ 37.93万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824660&HistoricalAwards=false
关键词：
Data based Iterative Control using

项目摘要

This grant will support research that will contribute new knowledge related to increased automation in high-demand, low-volume manufacturing sectors, such as aerospace. In contrast to full automation, there is a need for growing-convergence research on semi-autonomous approaches for low-volume manufacturing, which exploit the combination of human adaptability and machine precision and speed, to be cost effective. Robots with series-elastic actuators (SEA) have soft joints, which enables precision control over the forces applied to the environment and are therefore, considered to be inherently safe for human-robot collaboration. This inherent safety facilitates easy adoption by workers who can directly program the robots by physical demonstrations, which in turn reduces the amount of training needed for new workers. Nevertheless, this increased control over forces comes at the cost of lower positioning precision, which limits their use in manufacturing, where precision is important. The results from this research will increase the precision of such inherently-safe robots, and enable their use by relatively-novice workers. Moreover, the use of robotic solutions for manufacturing in confined spaces, rather than a human crawling inside, can lead to thinner, lighter and more efficient aircraft wings, with lower operating costs. Thus, the work will directly impact US competitiveness in the aerospace manufacturing sector with a substantial number of high-paying jobs. This research involves the integration of control theory and advanced robotics in manufacturing. Due to substantial and growing interest in manufacturing and robotics, the efforts will help to increase participation by underrepresented groups in research, and strengthen engineering education.Relatively-soft, series elastic actuators along with low-impedance control improves control authority over the force exerted by such robots on the environment, and has the potential to enable human-robot collaboration in the manufacturing environment. Nevertheless, a central issue is that the flexural systems in such robots result in non-minimum phase dynamics and high gains (for improved precision) can lead to instability. Moreover, accurate modeling for increased precision can be challenging due to substantial friction nonlinearities, backlash, and contact-related effects in series elastic actuators robots. This research will fill the knowledge-gap on data-based iterative machine learning approaches to improve the precision of such systems. The research will use uncertainty estimates from the kernel-based learning approach to develop conditions on the size of the iteration gain for guaranteed convergence. The approach will be experimentally evaluated with a confined-space manufacturing testbed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这笔赠款将支持研究，这将有助于在高需求，小批量制造业，如航空航天领域增加自动化相关的新知识。与完全自动化相比，需要对小批量制造的半自主方法进行增长收敛研究，这种方法利用人类适应性与机器精度和速度的结合，以实现成本效益。具有串联弹性致动器（SEA）的机器人具有软关节，可以精确控制施加到环境的力，因此被认为是人机协作的固有安全性。这种固有的安全性便于工人通过物理演示直接对机器人进行编程，从而减少了新工人所需的培训量。尽管如此，这种对力的控制的增强是以较低的定位精度为代价的，这限制了它们在精度很重要的制造业中的使用。这项研究的结果将提高这种固有安全机器人的精度，并使其能够被相对新手的工人使用。此外，使用机器人解决方案在密闭空间中进行制造，而不是人类在内部爬行，可以导致更薄，更轻，更高效的飞机机翼，降低运营成本。因此，这项工作将直接影响美国在航空航天制造业的竞争力，该行业拥有大量高薪工作。这项研究涉及控制理论和先进机器人技术在制造业中的整合。由于对制造业和机器人技术的兴趣越来越大，这些努力将有助于提高未被充分代表的群体在研究中的参与，并加强工程教育。相对柔软的串联弹性致动器沿着低阻抗控制，提高了对此类机器人对环境施加的力的控制权限，并有可能使制造环境中的人机协作成为可能。然而，一个核心问题是，在这样的机器人的弯曲系统的结果在非最小相位动态和高增益（提高精度）可能会导致不稳定。此外，由于串联弹性致动器机器人中的大量摩擦非线性、间隙和接触相关效应，提高精度的精确建模可能具有挑战性。这项研究将填补基于数据的迭代机器学习方法的知识空白，以提高此类系统的精度。该研究将使用基于内核的学习方法的不确定性估计来制定迭代增益大小的条件，以保证收敛。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MIMO ILC using complex-kernel regression and application to Precision SEA robots

使用复杂内核回归的 MIMO ILC 及其在 Precision SEA 机器人中的应用

DOI：
10.1016/j.automatica.2021.109550
发表时间：
2021
期刊：
Automatica
影响因子：
6.4
作者：
Yan, Leon;Banka, Nathan;Owan, Parker;Piaskowy, Walter Tony;Garbini, Joseph L.;Devasia, Santosh
通讯作者：
Devasia, Santosh

Precision Data-enabled Koopman-type Inverse Operators for Linear Systems

线性系统的精确数据支持库普曼型逆算子