preprints.org > engineering > control and systems engineering > doi: 10.20944/preprints202405.1817.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 May 2024 / Approved: 28 May 2024 / Online: 28 May 2024 (05:35:47 CEST)

Soft robotic manipulators consisting of serially-stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for applications such as minimally invasive surgery and for inspections in confined environments. The control of these robots, however, remains challenging, due to the difficulty in accurately modelling the robots, in coping with their redundancies, and in solving their full inverse kinematics. In this work, we explore a hybrid approach to control serial soft robotic manipulators that combines machine learning (ML) to estimate the inverse kinematics with a closed-loop control to compensate the remaining errors. For the ML part, we compare various approaches, including both kernel-based learning and more general neural networks. We validate the selected ML model experimentally. For the closed-loop control part, we first explore Jacobian formulations using both synthetic models and numerical approximations from experimental data. We then implement integral control actions using both these Jacobians, and evaluate them experimentally. In an experimental validation, we demonstrate that the hybrid control approach achieves setpoint regulation in a robot with 6 inputs and 4 outputs.

Soft robotics; machine learning; closed-loop control

Engineering, Control and Systems Engineering

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