Adaptive neural network Dynamic Surface Control: An evaluation on the musculoskeletal robot Anthrob
Adaptive neural network Dynamic Surface Control: An evaluation on the musculoskeletal robot Anthrob
复制标题
自适应神经网络动态表面控制：对肌肉骨骼机器人 Anthrob 的评估
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Knoll
中科院分区：
文献类型：
--
作者：
Michael Jäntsch;Steffen Wittmeier;K. Dalamagkidis;G. Herrmann;A. Knoll
软机器人方法被广泛认为是使机器人在不久的将来能够离开笼子，在我们的现代家庭和制造场所自由移动。肌肉骨骼机器人是这样的软机器人，其具有被动顺应驱动的功能，同时利用肌腱驱动系统的优势。尽管这些机器人在过去的十年里得到了深入的研究，但高性能的反馈控制律只是最近才被开发出来。在[1]中，利用动态表面控制（DSC）开发了一种控制器，该控制器是反推的扩展，具有关节和肌肉摩擦的自适应神经网络补偿器。我们比较这些新的控制策略计算力控制（CFC），现有的技术从肌腱驱动控制领域，产生高度改善的轨迹跟踪。肌肉骨骼机器人Anthrob [2]作为基准。
The soft robotics approach is widely considered to enable robots in the near future to leave their cages and move freely in our modern homes and manufacturing sites. Musculoskeletal robots are such soft robots which feature passively compliant actuation, while leveraging the advantages of tendon-driven systems. Even though these robots have been intensively researched within the last decade, high-performance feedback control laws have only very recently been developed. In [1], a controller was developed utilizing Dynamic Surface Control (DSC), an extension to backstepping, with an adaptive neural network compensator for joint as well as muscle friction. We compare these novel control strategies to Computed Force Control (CFC), an existing technique from the field of tendon-driven control, yielding highly improved trajectory tracking. The musculoskeletal robot Anthrob [2] serves as a benchmark.
影响因子：
2.4
作者：
Thelen, DG;Anderson, FC;Delp, SL
通讯作者：
Delp, SL