前馈
机器人
稳健性(进化)
控制工程
弹道
控制理论(社会学)
计算机科学
鲁棒控制
组分(热力学)
控制(管理)
控制系统
工程类
人工智能
热力学
基因
电气工程
物理
生物化学
化学
天文
作者
Franco Angelini,Cosimo Della Santina,Manolo Garabini,Matteo Bianchi,Gian Maria Gasparri,Giorgio Grioli,Manuel G. Catalano,Antonio Bicchi
出处
期刊:IEEE Transactions on Robotics
[Institute of Electrical and Electronics Engineers]
日期:2018-05-31
卷期号:34 (4): 924-935
被引量:54
标识
DOI:10.1109/tro.2018.2830351
摘要
Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.
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