Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints |
| |
| Y. Yang, J. T. Han, Z. J. Liu, Z. J. Zhao, and K.-S. Hong, “Modeling and adaptive neural network control for a soft robotic arm with prescribed motion constraints,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 501–511, Feb. 2023. doi: 10.1109/JAS.2023.123213 |
| |
| Authors: | Yan Yang Jiangtao Han Zhijie Liu Zhijia Zhao Keum-Shik Hong |
| |
| Affiliation: | 1. School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 10083, and also with the Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China;2. School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China;3. School of Mechanical Engineering, Pusan National University, Busan 46241, South Korea |
| |
| Abstract: | This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm. |
| |
| Keywords: | Adaptive control cosserat theory prescribed motion constraints soft robotic arm |
|
| 点击此处可从《IEEE/CAA Journal of Automatica Sinica》浏览原始摘要信息 |
|
点击此处可从《IEEE/CAA Journal of Automatica Sinica》下载免费的PDF全文 |
|
