A miniature pneumatic bending rubber actuator controlled by using the PSO-SVR-based motion estimation method with the generalized Gaussian Kernel

Kou Fujita; Mingcong Deng; Shuichi Wakimoto

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

Kou Fujita, Mingcong Deng, Shuichi Wakimoto

School of Engineering

Research output: Contribution to journal › Article › peer-review

23 Citations (Scopus)

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.

Original language	English
Article number	6
Journal	Actuators
Volume	6
Issue number	1
DOIs	https://doi.org/10.3390/act6010006
Publication status	Published - Mar 1 2017

Keywords

Nonlinear control
Operator theory
Particle swarm optimization
Soft actuator
Support vector machine
Support vector regression

ASJC Scopus subject areas

Control and Systems Engineering
Control and Optimization

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10.3390/act6010006

Cite this

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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.",

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AU - Fujita, Kou

AU - Deng, Mingcong

AU - Wakimoto, Shuichi

N1 - Funding Information: We are grateful to the specially appointed senior assistant Shin Wakitani of Hiroshima University for his suggestions and comments. Publisher Copyright: © 2017 by the authors.

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Y1 - 2017/3/1

N2 - 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.

AB - 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.

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A miniature pneumatic bending rubber actuator controlled by using the PSO-SVR-based motion estimation method with the generalized Gaussian Kernel

Abstract

Keywords

ASJC Scopus subject areas

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