TY - GEN
T1 - Control-oriented modeling of soft robotic swimmer with koopman operators
AU - Castano, Maria L.
AU - Hess, Andrew
AU - Mamakoukas, Giorgos
AU - Gao, Tong
AU - Murphey, Todd
AU - Tan, Xiaobo
N1 - Funding Information:
*This research was supported in part by the National Science Foundation (DGE1424871,IIS 1715714, IIS 1848945, CBET1702987) and an MSU Strategic Partnership Grant (16-SPG-Full-3236).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Interest in soft robotics has increased in recent years due to their potential in a myriad of applications. A wide variety of soft robots has emerged, including bio-inspired robotic swimmers such as jellyfish, rays, and robotic fish. However, the highly nonlinear fluid-structure interactions pose considerable challenges in the analysis, modeling, and feedback control of these soft robotic swimmers. In particular, developing models that are of high fidelity but are also amenable to control for such robots remains an open problem. In this work, we propose a data-driven approach that exploits Koopman operators to obtain a linear representation of the soft swimmer dynamics. Specifically, two methodologies are explored for obtaining the basis functions of the the operator, one based on data-based derivatives estimated using high-gain observers, and the other based on the dynamics structure of a tail-actuated rigid-body robotic fish. The resulting approximate finite-dimensional operators are trained and evaluated using data from high-fidelity CFD simulations that incorporate fluid-structure interactions. Validation results demonstrate that, while both methods are promising in producing control-oriented models, the approach based on derivative estimates shows higher accuracy in state prediction.
AB - Interest in soft robotics has increased in recent years due to their potential in a myriad of applications. A wide variety of soft robots has emerged, including bio-inspired robotic swimmers such as jellyfish, rays, and robotic fish. However, the highly nonlinear fluid-structure interactions pose considerable challenges in the analysis, modeling, and feedback control of these soft robotic swimmers. In particular, developing models that are of high fidelity but are also amenable to control for such robots remains an open problem. In this work, we propose a data-driven approach that exploits Koopman operators to obtain a linear representation of the soft swimmer dynamics. Specifically, two methodologies are explored for obtaining the basis functions of the the operator, one based on data-based derivatives estimated using high-gain observers, and the other based on the dynamics structure of a tail-actuated rigid-body robotic fish. The resulting approximate finite-dimensional operators are trained and evaluated using data from high-fidelity CFD simulations that incorporate fluid-structure interactions. Validation results demonstrate that, while both methods are promising in producing control-oriented models, the approach based on derivative estimates shows higher accuracy in state prediction.
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U2 - 10.1109/AIM43001.2020.9159033
DO - 10.1109/AIM43001.2020.9159033
M3 - Conference contribution
AN - SCOPUS:85090397097
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 1679
EP - 1685
BT - 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
Y2 - 6 July 2020 through 9 July 2020
ER -