A constitutive artificial neural networks-based mechanical model of the pneumatic artificial muscles

被引：0

作者：

Wang, Shuopeng ^{[1
]}

Wang, Rixin ^{[1
]}

Ma, Binwu ^{[1
]}

Zhang, Ying ^{[1
]}

Hao, Lina ^{[1
]}

机构：

[1] Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Peoples R China

来源：

PHYSICA SCRIPTA | 2025年 / 100卷 / 02期

基金：

中国国家自然科学基金;

关键词：

constitutive artificial neural networks; pneumatic artificial muscle; mechanical model; continuum mechanics; TRACKING CONTROL; HYSTERESIS; SYSTEMS;

D O I：

10.1088/1402-4896/ada30e

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Pneumatic artificial muscles (PAMs), recognized as typical smart material actuators, have perennially presented a formidable challenge in the realm of precise mechanical modeling due to the hyperelasticity and nonlinearity. In order to construct the mechanical model of the PAM, we propose a constitutive artificial neural network-based mechanical model. Utilizing the constitutive artificial neural network (CANN), we have constructed a strain energy function for PAMs that satisfies symmetry, objectivity, and polyconvexity. Furthermore, by employing the principle of virtual work and considering the hyper-elastic material, the geometric constraints, and the deformation of the internal air chamber, we have derived the mechanical model of PAMs. To verify the accuracy of the proposed model, the finite element simulation is used to demonstrate the modeling accuracy under different load conditions for PAMs with different geometries and constitutive model conditions. Finally, the accuracy and generalization of the proposed model is validated through experiments on a PAM experimental platform.

引用

页数：12