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Model-Based Data-Driven System Identification and Controller Synthesis Framework for Precise Control of SISO and MISO HASEL-Powered Robotic SystemsSoft 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.Free, publicly-accessible full text available April 1, 2023
System Identification and Closed-Loop Control of a Hydraulically Amplified Self-Healing Electrostatic (HASEL) ActuatorThis 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.