Model-Based Data-Driven System Identification and Controller Synthesis Framework for Precise Control of SISO and MISO HASEL-Powered Robotic Systems

Soft robots require a complimentary control architecture to support their inherent compliance and versatility. This work presents a framework to control soft-robotic systems systematically and effectively. The data-driven model-based approach developed here makes use of Dynamic Mode Decomposition wi...

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Bibliographic Details
Published in2022 IEEE 5th International Conference on Soft Robotics (RoboSoft) pp. 209 - 216
Main Authors Volchko, Angella, Mitchell, Shane K., Morrissey, Timothy G., Humbert, J. Sean
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.04.2022
Subjects
Control systems
Loading
Manipulators
MISO communication
Soft robotics
Steady-state
System identification
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Summary:Soft robots require a complimentary control architecture to support their inherent compliance and versatility. This work presents a framework to control soft-robotic systems systematically and effectively. The data-driven model-based approach developed here makes use of Dynamic Mode Decomposition with control (DMDc) and standard controller synthesis techniques. These methods are implemented on a robotic arm driven by an antagonist pair of Hydraulically Amplified Self-Healing Electrostatic (HASEL) actuators. The results demonstrate excellent tracking performance and disturbance rejection, achieving a steady state error under 0.25% in response to step inputs and maintaining a reference orientation within 0.5 degrees during loading and unloading. The procedure presented in this work can be extended to develop effective and robust controllers for other soft-actuated systems without knowledge of their dynamics a priori.
DOI:10.1109/RoboSoft54090.2022.9762220