@inproceedings{4a00b3cfd4d34d98961aae9dc1fde40f,
title = "An Improved Adaptive Sliding Mode Control of a Robotic Manipulator with Hysteresis Nonlinearity Based on Adaptive Speed Factor",
abstract = "This paper presents a novel adaptive sliding mode control design for a robotic manipulator with backlash hysteresis nonlinearity and uncertainties in actuator dynamics. A radial basis function (RBF) neural network (NN) is used to approximate the dynamic robotic terms and the robust term in the proposed adaptive sliding mode controller can compensate for the hysteresis nonlinearity. Aiming at the online-tuning problem for proportional-differential (PD) controller gains, the self-coupling PD control with adaptive speed factor (ASF) is proposed, which helps the system to achieve better performance on the tracking and smoother torque outputs. And the comparison with three different controllers is presented in the paper. Theoretical analysis and simulation results have proved the effectiveness of the control scheme.",
keywords = "ASF, NN control, adaptive control, backlash hysteresis",
author = "Ling Tan and Guodong Li and Jian Li and Kexin Hu and Ruiwei Dong and Minshan Feng and Liguo Zhu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2021 ; Conference date: 09-07-2021 Through 11-07-2021",
year = "2021",
doi = "10.1109/MLISE54096.2021.00107",
language = "English",
series = "Proceedings - 2021 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "515--520",
booktitle = "Proceedings - 2021 International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2021",
address = "United States",
}