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.
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Acknowledgments
This work was supported by Fundamental Research Funds of Shenzhen Science and Technology Project (JCYJ20160229172341417), Natural Science Basic Research Plan in Shaanxi Province (2016KJXX-86), Aeronautical Science Foundation of China (2015ZA53003) and National Natural Science Foundation of China (61622308).
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Cheng, Y., Shao, T., Zhang, R., Xu, B. (2017). Composite Learning Control of Hypersonic Flight Dynamics Without Back-Stepping. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_23
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DOI: https://doi.org/10.1007/978-3-319-70136-3_23
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