Adaptive Neural CPG-Based Control for a Soft Robotic Tentacle

Marlene Hammer Jeppesen, Jonas Jørgensen, Poramate Manoonpong*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Soft robotics is an area that is promising with its vast application space. One of the challenging aspects of this branch of robotics is the control of soft structures. This paper proposes a neural central pattern generator (CPG) based control architecture using an amplitude-adaptive oscillator for the movement of a low cost, pneumatically actuated soft robotic tentacle with three air chambers. The CPG is created using an SO(2) oscillator that generates half-sinusoidal outputs for pneumatic control. Through the use of an adaptation mechanism, the Dual Integral Learner (DIL), the parameters of the CPG are modulated to generate oscillatory signals of larger or smaller amplitude upon external perturbations to the system. The proposed neural control is implemented on the physical system and its validity is tested through physical restriction of the pneumatic air supply to the soft robotic tentacle.
Original languageEnglish
Title of host publicationNeural Information Processing. ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Publication date2020
ISBN (Print)9783030638320
ISBN (Electronic)978-3-030-63833-7
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18. Nov 202022. Nov 2020


Conference27th International Conference on Neural Information Processing, ICONIP 2020
SeriesLecture Notes in Computer Science


  • Soft robotics
  • Neurorobotics
  • Adaptive control
  • Neurodynamics
  • Plasticity


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