scholarly journals Erratum: Martens, M.; Boblan, I. Modeling the Static Force of a Festo Pneumatic Muscle Actuator: A New Approach and a Comparison to Existing Models. Actuators 2017, 6, 33

Actuators ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 9 ◽  
Author(s):  
Mirco Martens ◽  
Ivo Boblan
2021 ◽  
Author(s):  
Saswath Ghosh ◽  
Deepak Kumar ◽  
Sitikantha Roy

Abstract The present study reports an amended static force model for a pneumatic muscle actuator (PMA) used in different aerodynamic and fluid power system applications. The PMA is a fluid actuator, made of a polymeric bladder enclosed in a braided mesh sleeve. A physics-based static model is developed to predict the deformation response of the actuator for different applied pressure. The significant losses, like braid-to-braid friction, non-cylindrical ends, and bladder hyperelasticity effect, have been considered to enhance the model’s practical feasibility. However, a combined effect of all these losses in the PMA was ignored in the literature. The findings of the derived model agree well with existing experimental results.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Zhong ◽  
Xu Zhou ◽  
Minzhou Luo

Pneumatic muscle actuators (PMAs) own excellent compliance and a high power-to-weight ratio and have been widely used in bionic robots and rehabilitated robots. However, the high nonlinear characteristics of PMAs due to inherent construction and pneumatic driving principle bring great challenges in applications acquired accurately modeling and controlling. To tackle the tricky problem, a single PMA mass setup is constructed, and a back propagation neural network (BPNN) is employed to identify the dynamics of the setup. An offline model is built up using sampled data, and online modifications are performed to further improve the quality of the model. An adaptive controller based on BPNN is designed using gradient descent information of the built-up model. Experiments of identifying the PMA setup using BPNN and position tracking by adaptive BPNN controller are performed, and results demonstrate the good capacity in accurate controlling of the PMA setup.


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