A proposed soft pneumatic actuator control based on angle estimation from data-driven model

Mohamed, Marghny H.; Wagdy, Soha H; Atalla, Mostafa A.; Youssef, Aliaa Rehan; Maged, Shady A.

doi:10.1177/0954411920911277

Cited by 23 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The middle, ring and little finger are designed similarly to the index, but with different lengths of the segments. The SPA was modeled on ABAQUS/CAE (2019, Dassault Systèmes®, Vélizy-Villacoublay, France) [12,13,14]. The model is drawn as 4 parts: tube, outer layer, clockwise cable, and anti-clockwise cables, as shown in Figure 6.…”

Section: Methodsmentioning

confidence: 99%

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove

2021

Self Cite

View full text Add to dashboard Cite

The soft pneumatic actuators of an assistive exoskeleton hand glove are here designed. The design of the actuators focuses on allowing the actuator to perform the required bending and to restrict elongation or twisting of the actuator. The actuator is then modeled using ABAQUS/CAE, a finite element modeling software, and the open loop response of the model is obtained. The parameters of the actuator are then optimized to reach the optimal parameters corresponding to the best performance. Design of experiment (DOE) techniques are then approached to study the robustness of the system. Software-in-the-loop (SiL) is then approached to control the model variables via a proportional-integral-derivative (PID) control generated by FORTRAN code. The link between the two programs is to be achieved by the user subroutine that is written, where the subroutine receives values from ABAQUS/CAE, performs calculations, and passes values back to the software. The controller’s parameters are tuned and then the closed loop response of the model is obtained by setting the desired bending angle and running the model. Furthermore, a concentrated force at the tip of the actuator is added to observe the actuator’s response to external disturbance.

show abstract

Section: Methodsmentioning

confidence: 99%

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…These models rely on training neural networks that are capable of predicting the deformation of the soft material and the position of the actuator tip or end-effector. Some proposed models are linear regression models [15]. One approach used simulation data from a Finite Element Method (FEM) hyperelastic material model to train an Artificial Neural Network (ANN) to predict the bending angles of SPAs with variable geometrical parameters [16].…”

Section: Related Workmentioning

confidence: 99%

Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data

et al. 2022

View full text Add to dashboard Cite

Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.

show abstract

“…The data-driven machine learning approach was also previously used for modeling the nonlinearity of the SPAs arising from variation in solenoid valve flow rates during pressurization and depressurization (Mohamed et al 2020;Mosadegh et al 2014). Such non-linearity in addition to the multi-material combination in the manufacturing, and effects of other embedded components in the body of SPAs, could not be addressed merely by FEA, thus causing difficulty in the accurate bending control of 3D/4D-printed SPAs in practice.…”

Section: Data-driven Machine Learning Modeling and Controlmentioning

confidence: 99%

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

Zolfagharian

Mahmud

Gharaie

et al. 2020

Virtual and Physical Prototyping

115

View full text Add to dashboard Cite

This article reviews soft pneumatic actuators (SPAs) that are manufactured entirely via additive manufacturing methods. These actuators are known as four-dimensional (4D)-printed SPAs and can generate bending motions in response to either pressurized or vacuum (negative pressure) air stimulus after fabrication. They are characterized by geometrical and material factors that determine their motion trajectory, and the force they exert on manipulated soft objects in delicate applications such as food handling and non-invasive surgery. Here, we review various 3D printers and materials used for the fabrication of the pressurized air bendingtype SPAs. The reported approaches for modeling and control of these actuators are presented and compared. General discussions, as well as future directions and challenges of these actuators, are given.

show abstract

A proposed soft pneumatic actuator control based on angle estimation from data-driven model

Cited by 23 publications

References 33 publications

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove

Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data

3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

Contact Info

Product

Resources

About