斯图尔特站台
人工神经网络
跟踪(教育)
控制(管理)
实时控制系统
离散时间和连续时间
计算机科学
人工智能
控制理论(社会学)
控制工程
工程类
数学
统计
心理学
物理
教育学
运动学
经典力学
作者
Yang Shi,Wangrong Sheng,Jie Wang,Yingqi Chen,Bin Li,Xiaobing Sun
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-13
标识
DOI:10.1109/tsmc.2024.3392848
摘要
rgb0.00,0.00,0.00 In recent years, the discrete-time recurrent neural network (DTRNN) model has received growing attention. This fully benefits from the recurrent neural networks (RNNs) that not only have plenty of advantages for solving computing problems in the real-time tracking control but also have the remarkable potential of parallel processing and nonlinear processing. However, there is a general lack of research on the applicability of DTRNN model to handle parallel robot. In addition, the precision is always an important point in real-time tracking control, and most of existing studies generally lack the elaborate researches on the precision analyses. In this article, the corresponding DTRNN model (i.e., general five-instant discretization (FID) formula DTRNN model) with parameter selection method is established. As one of the important theoretical contributions, the dominant term of truncation error of discretization formula and the conditions of maintaining precision of corresponding DTRNN model are proved from the mathematical view strictly. Besides, the influence of the selected parameter for the precision of such a DTRNN model is also analyzed. Finally, the above theoretical analyses are verified in the tracking control experiments of the Stewart platform, which is a widely used and representative parallel robot.
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