@inproceedings{d89ec6ec4c0543a28d352a2263eabb86,
title = "Trajectory Tracking Control Based on RBF Neural Network Learning Control",
abstract = "In this paper, a radial basis function neural network (RBFNN) learning control scheme is proposed to improve the trajectory tracking performance of a 3-DOF robot manipulator based on deterministic learning theory, which explains the parameter convergence phenomenon in the adaptive neural network control process. A new kernel function is proposed to replace the original Gaussian kernel function in the network, such that the learning speed and accuracy can be improved. In order to make more efficient use of network nodes, this paper proposes a new node distribution strategy. Based on the improved scheme, the tracking accuracy of the 3-DOF manipulator is improved, and the convergence speed of the network is improved.",
keywords = "3-DOF manipulator, Deterministic learning, RBFNN, Trajectory tracking control",
author = "Chengyu Han and Yiming Fei and Zixian Zhao and Jiangang Li",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",
year = "2022",
doi = "10.1007/978-3-031-13841-6\_38",
language = "英语",
isbn = "9783031138409",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "410--421",
editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",
booktitle = "Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings",
address = "德国",
}