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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.more » « less
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Ly, Khoi; Kellaris, Nicholas; McMorris, Dade; Johnson, Brian K.; Acome, Eric; Sundaram, Vani; Naris, Mantas; Humbert, J. Sean; Rentschler, Mark E.; Keplinger, Christoph; et al (, Soft Robotics)null (Ed.)
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Schunk, Cosima; Pearson, Levi; Acome, Eric; Morrissey, Timothy G.; Correll, Nikolaus; Keplinger, Christoph; Rentschler, Mark E.; Humbert, J. Sean (, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))This paper describes a system identification method and the development of a closed-loop controller for a Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator. Our efforts focus on developing a reliable and consistent way to identify system models for these soft robotic actuators using high-speed videography based motion tracking. Utilizing a mass-spring-damper model we are able to accurately capture the behavior of a HASEL actuator. We use the resulting plant model to design a Proportional-Integral controller that demonstrates improved closed-loop tracking and steady-state error performance.more » « less
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