Design Optimization of a Miniaturized Pneumatic Artificial Muscle and Experimental Validation

Author:

Zabihollah Shakila1ORCID,Moezi Seyed Alireza1ORCID,Sedaghati Ramin1ORCID

Affiliation:

1. Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada

Abstract

Miniaturized pneumatic artificial muscles (MPAMs) are widely utilized in various applications due to their unique characteristics, such as a high power-to-weight ratio, flexibility, and compatibility with the human environment, as well as being compact enough to fit within small-scale mechanical systems. Maximizing the amount of force generated by these actuators while keeping their dimensions minimized can greatly affect their efficiency. In this study, a formal design optimization problem was formulated to identify optimal sizes of MPAMs while maximizing their blocked force as a novel approach to address the issue of low force outputs of these actuators. A force model for an MPAM including various correction terms was derived to better predict the response behavior of the actuator. The optimization results reveal that an MPAM with a bladder that has an outer diameter of 6 mm and a thickness of 0.7 mm, as well as a braid angle of 72 degrees, can produce up to almost 239 N of blocked force if the inlet pressure is increased to 600 kPa. An MPAM with optimal parameters was subsequently fabricated and experimentally tested to evaluate its quasi-static response behavior and to validate the theoretical optimization results. Experimental tests were conducted under a wide range of pressures (0–300 kPa) to evaluate the variation of the generated blocked force versus inlet pressure. The overall error between the simulation and the experimental blocked forces was found to be less than 10%. This study represents a significant contribution to the design optimization of MPAMs, and the resulting optimal design offers potential applications in various fields, from soft robots to medical devices.

Funder

Natural Sciences and Engineering Research Council

Publisher

MDPI AG

Subject

Control and Optimization,Control and Systems Engineering

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