Model-Free Control of a Soft Pneumatic Segment

Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour...

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Bibliographic Details
Published in:Biomimetics (Basel, Switzerland) Switzerland), 2024-02, Vol.9 (3), p.127
Main Authors: García-Samartín, Jorge Francisco, Molina-Gómez, Raúl, Barrientos, Antonio
Format: Article
Language:English
Subjects:
Analysis
Biosensors
Elastomers
Health aspects
machine learning
Methods
model-free control
Neural networks
pneumatic robot
Robotics
Robots
Sensors
soft arm
soft robots
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Summary:Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL's performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments.
ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics9030127