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Modelling, Design Optimization, and Experimental Characterization of Miniaturized Pneumatic Artificial Muscles


Modelling, Design Optimization, and Experimental Characterization of Miniaturized Pneumatic Artificial Muscles

Zabihollah, Shakila (2023) Modelling, Design Optimization, and Experimental Characterization of Miniaturized Pneumatic Artificial Muscles. Masters thesis, Concordia University.

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Miniaturized pneumatic artificial muscles (MPAMs) are actuators designed to replicate the actuation behaviour of natural muscles. Their unique characteristics, including a high power-to- weight ratio, flexibility, compatibility with the human environment, and compact size, make them widely used in diverse applications. However, MPAMs face a significant challenge in terms of their low force output, which hinders their overall performance. Enhancing their force generation capability while maintaining their compact dimensions is crucial for improving their efficiency.
The present thesis focuses on the design optimization, fabrication, and modelling of an MPAM to maximize its force output while ensuring compatibility with small-scale applications. To this end, a formal design optimization problem is formulated to determine the optimal sizes of MPAMs, with the objective of maximizing their blocked force under geometrical constraints. A comprehensive force model is derived, considering key parameters that influence the response behaviour of MPAMs, which serves as the objective function for maximization. To investigate the importance of various correction terms added to the simple force model of the MPAMs, two optimization formulations varying in their objective functions and vectors of design variables have been defined. One formulation considers the effects of energy stored in the braided sleeving and optimizes the parameters related to braid strands, while the other excludes these factors. To identify the optimal design, a hybrid optimization algorithm is employed, combining a stochastic-based algorithm with gradient-based algorithms. This approach allows for the identification of the global optimum while also examining the effects of different optimization algorithms on the results.
Next, two MPAMs are fabricated using the dimensions obtained from the optimization procedure. The first sample utilizes Ecoflex-50 as the bladder material, while the second sample incorporates a mixture of PDMS and Ecoflex-30. The aim is to compare the performance of the MPAMs
fabricated with different materials for their bladders. An experimental setup is subsequently designed to conduct quasi-static tests on each sample to measure their generated blocked force and contraction under various pressures as well as validate the theoretical results obtained from the optimization process.
Finally, the hysteresis loops obtained from loading and unloading each sample under specific pressures are analyzed to derive correction terms that account for the nonlinear behaviour of MPAMs and the friction between their components. Different theoretical and empirical approaches are assessed to determine the most accurate correction terms. The resulting force model enables accurate predictions of force and contraction outputs under various inlet pressures.
Overall, this study contributes significantly to the design optimization of MPAMs, offering potential applications in diverse fields, including soft robotics and medical devices. The combination of theoretical modelling, optimization techniques, fabrication, and experimental tests provides essential guide for the comprehensive understanding of MPAM’s performance and its potential for practical implementation.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:Zabihollah, Shakila
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical Engineering
Date:20 July 2023
Thesis Supervisor(s):Sedaghati, Ramin
ID Code:992579
Deposited By: Shakila Zabihollah
Deposited On:17 Nov 2023 14:31
Last Modified:17 Nov 2023 14:31
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