Design of a 2dof ankle exoskeleton with a polycentric structure and a bi-directional tendon-driven actuator controlled using a pid neural network

Taehoon Lee, Inwoo Kim, Yoon Su Baek

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Lower limb exoskeleton robots help with walking movements through mechanical force, by identifying the wearer’s walking intention. When the exoskeleton robot is lightweight and comfortable to wear, the stability of walking increases, and energy can be used efficiently. However, because it is difficult to implement the complex anatomical movements of the human body, most are designed simply. Due to this, misalignment between the human and robot movement causes the wearer to feel uncomfortable, and the stability of walking is reduced. In this paper, we developed a two degrees of freedom (2DoF) ankle exoskeleton robot with a subtalar joint and a talocrural joint, applying a four-bar linkage to realize the anatomical movement of a simple 1DoF structure mainly used for ankles. However, bidirectional tendon-driven actuators (BTDAs) do not consider the difference in a length change of both cables due to dorsiflexion (DF) and plantar flexion (PF) during walking, causing misalignment. To solve this problem, a BTDA was developed by considering the length change of both cables. Cable-driven actuators and exoskeleton robot systems create uncertainty. Accordingly, adaptive control was performed with a proportional-integral-differential neural network (PIDNN) controller to minimize system uncertainty.

Original languageEnglish
Article number9
Pages (from-to)1-17
Number of pages17
JournalActuators
Volume10
Issue number1
DOIs
Publication statusPublished - 2021 Jan

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Design of a 2dof ankle exoskeleton with a polycentric structure and a bi-directional tendon-driven actuator controlled using a pid neural network'. Together they form a unique fingerprint.

Cite this