State Estimation and Control with a Robust Extended Kalman Filter for a Fabric Soft Robot

Kyle Stewart, Zhi Qiao, Wenlong Zhang

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

Soft robots have shown great potential in safe human-robot interaction. However, their compliant nature makes posture measurement and trajectory tracking a challenging task. Accurate sensing systems such as motion capture devices aren't portable enough to use in any environment, while body-integrated soft sensors create problems like hysteresis. In this work, a robust extended Kalman filter (REKF) based sensor fusion method with an accelerometer, gyroscope, and draw wire sensor is introduced to estimate the bending angle of a fabric-based inflatable bending actuator. The REKF improves upon similar estimation techniques by ensuring a robust estimate regardless of non-linearity or uncertainty in the model. Linear Quadratic Gaussian (LQG) control is integrated with the proposed REKF to demonstrate the REKF based sensor fusion method is useful in control. The results show that the REKF based sensor fusion system presents a portable, robust, accurate estimation of the actuator's bending angle.

Original languageEnglish (US)
Pages (from-to)25-30
Number of pages6
JournalIFAC-PapersOnLine
Volume55
Issue number37
DOIs
StatePublished - 2022
Externally publishedYes
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: Oct 2 2022Oct 5 2022

Keywords

  • Extended Kalman Filter
  • Linear Quadratic Gaussian Control
  • Pneumatic Actuator
  • Soft Robotics
  • State Estimation

ASJC Scopus subject areas

  • Control and Systems Engineering

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