Next Article in Journal
Applying Standard Industrial Components for Active Magnetic Bearings
Next Article in Special Issue
Static and Dynamic Studies of Electro-Active Polymer Actuators and Integration in a Demonstrator
Previous Article in Journal
Re-Engineering a High Performance Electrical Series Elastic Actuator for Low-Cost Industrial Applications
Previous Article in Special Issue
Hysteresis Curve Fitting Optimization of Magnetic Controlled Shape Memory Alloy Actuator
Article Menu

Export Article

Open AccessFeature PaperArticle
Actuators 2017, 6(1), 6; doi:10.3390/act6010006

A Miniature Pneumatic Bending Rubber Actuator Controlled by Using the PSO-SVR-Based Motion Estimation Method with the Generalized Gaussian Kernel

1
Department of Electrical and Electronic Engineering, Graduate School of Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo 184-8588, Japan
2
Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama 700-8530, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Jose Luis Sanchez-Rojas
Received: 24 November 2016 / Revised: 19 January 2017 / Accepted: 20 January 2017 / Published: 3 February 2017
(This article belongs to the Special Issue MEMS-based Actuators)
View Full-Text   |   Download PDF [818 KB, uploaded 3 February 2017]   |  

Abstract

Soft actuators have been employed in various fields recently. A miniature pneumatic bending rubber actuator is one of the soft actuators. This actuator will be used for medical and biological fields. Its flexibility and high safety are suitable for fragile objects. However, its modeling is difficult due to its nonlinearity. There are no suitable sensors to measure the output of this actuator. In this paper, the particle swarm optimization-support vector regression (PSO-SVR)-based estimation method with the generalized Gaussian kernel is proposed. An experimental result with the operator-based robust nonlinear control system is employed to verify the effectiveness of the proposed method. View Full-Text
Keywords: support vector machine; support vector regression; particle swarm optimization; soft actuator; operator theory; nonlinear control support vector machine; support vector regression; particle swarm optimization; soft actuator; operator theory; nonlinear control
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Fujita, K.; Deng, M.; Wakimoto, S. A Miniature Pneumatic Bending Rubber Actuator Controlled by Using the PSO-SVR-Based Motion Estimation Method with the Generalized Gaussian Kernel. Actuators 2017, 6, 6.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Actuators EISSN 2076-0825 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top