Advanced Technologies in Soft Pneumatic Actuators

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuator Materials".

Deadline for manuscript submissions: 28 June 2024 | Viewed by 323

Special Issue Editor


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Guest Editor
Systems and Control Engineering, Università degli studi Magna Graecia di Catanzaro, Catanzaro, Italy
Interests: biomechatronics; human-robot interaction control; soft robotics; biomimetic actuators; nonlinear systems
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Special Issue Information

Dear Colleagues,

I am pleased to announce the Special Issue “Advanced Technologies in Soft Pneumatic Actuators” to be published in Actuators.

Newly introduced design approaches, which integrate pneumatic actuation technologies with the adoption of soft hyper-elastic materials and novel processes of fabrication of soft actuators, pave the way for more efficient implementations of control and automation systems in a large range of industrial and automation processes and bio-robotic applications.

A common feature of all those important applications that can benefit from the adoption of a soft pneumatic actuation is the improved control performance, e.g., thanks to the low weight-to-power ratio and intrinsic safety of the actuators and soft robots interacting with a human subject, in collaborative robotics as well as in assistive and rehabilitation robotics mediated by soft wearable exoskeletons.

The Special Issue covers both theoretical and experimental challenges involved in the design, realization, and control of pneumatic soft actuators for all relevant applications of robotics and automation, control engineering, and healthcare and biomedical engineering.

Dr. Alessio Merola
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pneumatic actuation
  • pneumatic control systems
  • fluidic actuators
  • soft robotics
  • compliant actuators

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Particle Swarm Optimisation-based dynamic modelling of Pneumatic Artificial Muscle Actuator
Authors: Dexter Felix Brown, Sheng Quan Xie
Affiliation: School of Electronic and Electrical Engineering, University of Leeds, LS2 9JT Leeds, U.K
Abstract: Pneumatic Artificial Muscles (PAMs) possess compliant properties desirable for certain applications such as prosthetics and robotic structures. However, this compliance along with their inherent nonlinear dynamics make them difficult to accurately model, and as such accurately control under certain control architecture. Common approaches to this problem include measuring the actuator’s physical properties and approximating a model based on these parameters and using deep learning methods to train a model with the actuator’s behaviours. This paper will introduce an optimisation-based modelling approach based on particle swarm optimisation (PSO) algorithm, as well as a dynamic model template which incorporates the changing properties of the actuator over time. The accuracy of these models will be validated experimentally.

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