Trajectory tracking of a one-DOF manipulator using multiple fishing line actuators by iterative learning control

Shu Ono, Ken Masuya, Kentaro Takagi, Kenji Tahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, an iterative learning control scheme for a trajectory tracking task using a one-DOF joint manipulator which is driven by multiple antagonistic fishing line artificial muscle actuators is proposed. The fishing line actuator is one of the soft actuators made by coiling and heating a twisted polymer fiber. It has attracted attention from those who would develop soft robotic devices because it is soft, light, and low-cost. It, however, has several drawbacks, e.g. output force limitation, strong nonlinearity, or energy efficiency, etc. To cope with these drawbacks, firstly a one-DOF manipulator driven by multiple antagonistic actuators is proposed to enhance its output force, and the energy efficiency is analyzed to investigate the relationship between the energy consumption and a number of activated fishing line actuator. Next, an iterative learning control scheme to accomplish a trajectory tracking task by the one-DOF manipulator is proposed to improve its control performance even though under the existence of unknown nonlinearities. The effectiveness of the proposed control scheme is demonstrated through several experiments.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Soft Robotics, RoboSoft 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages467-472
Number of pages6
ISBN (Electronic)9781538645161
DOIs
Publication statusPublished - Jul 5 2018
Event1st IEEE International Conference on Soft Robotics, RoboSoft 2018 - Livorno, Italy
Duration: Apr 24 2018Apr 28 2018

Publication series

Name2018 IEEE International Conference on Soft Robotics, RoboSoft 2018

Conference

Conference1st IEEE International Conference on Soft Robotics, RoboSoft 2018
CountryItaly
CityLivorno
Period4/24/184/28/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Optimization
  • Modelling and Simulation
  • Mechanical Engineering

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