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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摘要Soft 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.
出版社接受日期26.01.2019
著者Zhiqiang Tang, Kelvin Ho Lam Heung, Raymond Kai Yu Tong, Zheng Li
會議名稱2019 International Conference on Robotics and Automation (ICRA)
會議開始日20.05.2019
會議完結日24.05.2019
會議地點Montreal
會議國家/地區加拿大
會議論文集題名Proceedings - IEEE International Conference on Robotics and Automation
出版年份2019
月份5
出版社IEEE
頁次4004 - 4010
國際標準期刊號10504729
語言美式英語

上次更新時間 2020-22-10 於 10:12