@inproceedings{f904077362e749e8b7d5c6346d41d0d0,
title = "Composite learning control of hypersonic flight dynamics without back-stepping",
abstract = "In this paper, composite neural control is proposed for hypersonic flight control in presence of unknown dynamics. Using high gain observer (HGO), the controller of attitude subsystem is designed without back-stepping. This strategy simplifies the process of controller design and reduces the computation burden of parameter updating. To construct the composite neural controller, the filtered modeling error is further considered in the weight updating of RBF NN. Moreover, the composite neural controller can achieve the fast learning of system uncertainty. Simulation is presented to demonstrate the effectiveness of the design.",
keywords = "Composite neural control, High gain observer, Hypersonic flight vehicle, Output-feedback control",
author = "Yixin Cheng and Tianyi Shao and Rui Zhang and Bin Xu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70136-3\_23",
language = "英语",
isbn = "9783319701356",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "212--218",
editor = "Derong Liu and Shengli Xie and Dongbin Zhao and Yuanqing Li and El-Alfy, \{El-Sayed M.\}",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
}