Reverse pneumatic artificial muscles (rPAMs): Modeling, integration, and control

被引:39
|
作者
Skorina, Erik H. [1 ]
Luo, Ming [1 ]
Oo, Wut Yee [2 ]
Tao, Weijia [1 ]
Chen, Fuchen [1 ]
Youssefian, Sina [3 ]
Rahbar, Nima [4 ]
Onal, Cagdas D. [1 ,3 ]
机构
[1] Worcester Polytech Inst, Robot Engn Program, Worcester, MA 01609 USA
[2] Stanford Univ, Dept Mech Engn, Stanford, CA 94305 USA
[3] Worcester Polytech Inst, Dept Mech Engn, Worcester, MA 01609 USA
[4] Worcester Polytech Inst, Dept Civil Engn, Worcester, MA 01609 USA
来源
PLOS ONE | 2018年 / 13卷 / 10期
关键词
SOFT; RUBBER;
D O I
10.1371/journal.pone.0204637
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Despite offering many advantages over traditional rigid actuators, soft pneumatic actuators suffer from a lack of comprehensive, computationally efficient models and precise embedded control schemes without bulky flow-control valves and extensive computer hardware. In this article, we consider an inexpensive and reliable soft linear actuator, called the reverse pneumatic artificial muscle (rPAM), which consists of silicone rubber that is radially constrained by symmetrical double-helix threading. We describe analytical and numerical static models of this actuator, and compare their performance against experimental results. To study the application of rPAMs to operate underlying kinematic linkage skeletons, we consider a single degree-of-freedom revolute joint that is driven antagonistically by two of these actuators. An analytical model is then derived, and its accuracy in predicting the static joint angle as a function of input pressures is presented. Using this analytical model, we perform dynamic characterization of this system. Finally, we propose a sliding-mode controller, and a sliding mode controller augmented by a feed-forward term to modulate miniature solenoid valves that control air flow to each actuator. Experiments show that both controllers function well, while the feed-forward term improves the performance of the controller following dynamic trajectories.
引用
收藏
页数:24
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