Adaptive neural network feedforward control for dynamically substructured systems

Guang Li, Jing Na, David P. Stoten, Xuemei Ren

科研成果: 期刊稿件文章同行评审

37 引用 (Scopus)

摘要

The potential applications of dynamically substructured systems (DSSs) with both numerical and physical substructures can be found in diverse dynamics testing fields. In this paper, an adaptive feedforward controller based on a neural network (NN) is proposed to improve the DSS testing performance. To facilitate the NN compensation design, a modified DSS framework is developed so that the DSS control can be considered as a regulation problem with disturbance rejection. Then an adaptive NN feedforward compensation technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature, and it does not require any information of the plant model and disturbance model, which significantly simplifies its application on DSS. In particular, we propose a novel adaptive law for the NN online learning, where appropriate NN weight error information is derived and used to achieve improved performance. Real-time experimental results on a mechanical test rig demonstrate the improved performance by using the NN compensation strategy and the new adaptation law.

源语言英语
文章编号6565375
页(从-至)944-954
页数11
期刊IEEE Transactions on Control Systems Technology
22
3
DOI
出版状态已出版 - 5月 2014

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Li, G., Na, J., Stoten, D. P., & Ren, X. (2014). Adaptive neural network feedforward control for dynamically substructured systems. IEEE Transactions on Control Systems Technology, 22(3), 944-954. 文章 6565375. https://doi.org/10.1109/TCST.2013.2271036