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This content will become publicly available on May 23, 2023

Title: Data-Driven Control for a Milli-Scale Spiral-Type Magnetic Swimmer using MPC
This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our previous study successfully implemented two non-model-based control algorithms for 3D path-following using PID and model reference adaptive controller (MRAC). This paper focuses on system identification using only experimental data and a model-based control strategy. Four system models were derived: (1) a physical estimation model, (2, 3) Sparse Identification of Nonlinear Dynamics (SINDY), linear system and nonlinear system, and (4) multilayer perceptron (MLP). All four system models were implemented as an estimator of a multi-step Kalman filter. The maximum required sensing interval was increased from 180 ms to 420 ms and the respective tracking error decreased from 9 mm to 4.6 mm. Finally, a Model Predictive Controller (MPC) implementing the linear SINDY model was tested for 3D path-following and shown to be computationally efficient and offers performances comparable to other control methods.
Authors:
; ; ;
Award ID(s):
1932572 1553063
Publication Date:
NSF-PAR ID:
10352910
Journal Name:
2022 International Conference on Robotics and Automation (ICRA)
Volume:
1
Issue:
1
Page Range or eLocation-ID:
9823 to 9830
Sponsoring Org:
National Science Foundation
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