Closed-loop control of soft robot based ...
Document type :
Communication dans un congrès avec actes
Title :
Closed-loop control of soft robot based on machine learning
Author(s) :
Zhou, Yuan [Auteur]
Shangaï Jiao Tong University [Shangaï]
Ju, Mingda [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Zheng, Gang [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Deformable Robots Simulation Team [DEFROST ]
Shangaï Jiao Tong University [Shangaï]
Ju, Mingda [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Zheng, Gang [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Deformable Robots Simulation Team [DEFROST ]
Conference title :
CCC 2019 - 38th Chinese Control Conference
City :
Guangzhou
Country :
Chine
Start date of the conference :
2019-07-27
Publisher :
IEEE
HAL domain(s) :
Informatique [cs]/Robotique [cs.RO]
English abstract : [en]
In this paper, we present a new strategy to control the soft robot with elastic behavior, piloted by 4 actuators. The main contribution of this work is the use of neural network to get approximated model soft robots, based ...
Show more >In this paper, we present a new strategy to control the soft robot with elastic behavior, piloted by 4 actuators. The main contribution of this work is the use of neural network to get approximated model soft robots, based on which a robust controller is then proposed. In this paper, we proved that if the approximated model satisfies certain conditions, then the proposed robust controller can always drive any given point of interest of the robot to the desired position, without knowing the exact model. Finally, the proposed result is experimented and validated by a 3D printed silicone soft robot.Show less >
Show more >In this paper, we present a new strategy to control the soft robot with elastic behavior, piloted by 4 actuators. The main contribution of this work is the use of neural network to get approximated model soft robots, based on which a robust controller is then proposed. In this paper, we proved that if the approximated model satisfies certain conditions, then the proposed robust controller can always drive any given point of interest of the robot to the desired position, without knowing the exact model. Finally, the proposed result is experimented and validated by a 3D printed silicone soft robot.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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