Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot

Ming Yang, Qingsong Ai, Chengxiang Zhu, Jie Zuo, Wei Meng, Quan Li, Sheng Q Xie

Research output: Contribution to journalArticle

17 Citations (Scopus)
1 Downloads (Pure)

Abstract

A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training.
However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant
ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper.
The human-robot external disturbance can be estimated by an observer, who is then used to adjust the
robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov
stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the
proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output
in real time during operation, resulting in a higher trajectory tracking accuracy and better response
performance especially in dynamic conditions.
Original languageEnglish
Article number66
Number of pages21
JournalSensors
Volume18
Issue number1
Early online date28 Dec 2017
DOIs
Publication statusPublished - 2018

Keywords

  • parallel robot
  • ankle rehabilitation
  • pneumatic muscles
  • disturbance estimation
  • adaptive sliding mode control

Fingerprint Dive into the research topics of 'Disturbance-Estimated Adaptive Backstepping Sliding Mode Control of a Pneumatic Muscles-Driven Ankle Rehabilitation Robot'. Together they form a unique fingerprint.

  • Cite this