2020/11/16 by David A. Haggerty, Haggerty, David A., Michael J. Banks +7 · 17 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Computer science #Control (management) #Control engineering #Control theory (sociology) #Controller (irrigation) #Degrees of freedom (physics and chemistry) #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Inertial frame of reference #Lattice Boltzmann Simulation Studies #Linear-quadratic regulator #Mathematics #Micro and Nano Robotics #Nonlinear system #Physics #Reduction (mathematics) #Robot #Robotic arm #Robotics (cs.RO) #Soft Robotics and Applications #Systems and Control (eess.SY) #Underactuation #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.07939
published in arXiv (Cornell University) (Cornell University) · Submitted to IEEE International Conference on Robotics and Automation, 2021
arxiv created 2020/11/16 · openalex publication_date 2020/11/16 · arxiv updated 2020/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Soft robots promise improved safety and capability over rigid robots when deployed in complex, delicate, and dynamic environments. However, the infinite degrees of freedom and highly nonlinear dynamics of these systems severely complicate their modeling and control. As a step toward addressing this open challenge, we apply the data-driven, Hankel Dynamic Mode Decomposition (HDMD) with time delay observables to the model identification of a highly inertial, helical soft robotic arm with a high number of underactuated degrees of freedom. The resulting model is linear and hence amenable to control via a Linear Quadratic Regulator (LQR). Using our test bed device, a dynamic, lightweight pneumatic fabric arm with an inertial mass at the tip, we show that the combination of HDMD and LQR allows us to command our robot to achieve arbitrary poses using only open loop control. We further show that Koopman spectral analysis gives us a dimensionally reduced basis of modes which decreases computational complexity without sacrificing predictive power.