A Novel Iterative Learning Model Predictive Control Method for Soft Bending Actuators
Refereed conference paper presented and published in conference proceedings

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AbstractSoft robots attract research interests worldwide. However, its control remains challenging due to the difficulty in sensing and accurate modeling. In this paper, we propose a novel iterative learning model predictive control (ILMPC) method for soft bending actuators. The uniqueness of our approach is the ability to improve model accuracy gradually. In this method, a pseudo-rigid-body model is used to take an initial guess of the bending behavior of the actuator and the model accuracy is improved with iterative learning. Compared with conventional model free iterative learning control (ILC), the proposed method significantly reduces the learning curve. Compared with the model predictive control (MPC), the proposed method does not rely on an accurate model and it will output a satisfactory model after the learning process. A soft-elastic composite actuator (SECA) is used to validate the proposed method. Both simulation and experimental results show that the proposed method outperforms the conventional MPC and ILC.
Acceptance Date26/01/2019
All Author(s) ListZhiqiang Tang, Kelvin Ho Lam Heung, Raymond Kai Yu Tong, Zheng Li
Name of Conference2019 International Conference on Robotics and Automation (ICRA)
Start Date of Conference20/05/2019
End Date of Conference24/05/2019
Place of ConferenceMontreal
Country/Region of ConferenceCanada
Proceedings TitleProceedings - IEEE International Conference on Robotics and Automation
Pages4004 - 4010
LanguagesEnglish-United States

Last updated on 2021-17-10 at 00:22