Study on adaptive BTT reentry speed depletion guidance law based on BP neural network

Zongzhun Zheng; Yongji Wang; Hao Wu

doi:10.1117/12.750391

15 November 2007 Study on adaptive BTT reentry speed depletion guidance law based on BP neural network

Zongzhun Zheng, Yongji Wang, Hao Wu

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881X (2007) https://doi.org/10.1117/12.750391
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Reentry guidance is one of the key technologies in hypersonic vehicle research field. In addition to the constraints on its final position coordinates, the vehicle must also impact the target from a specified direction with high precision. And therefore the adaptability of guidance law is critical to control the velocity of hypersonic vehicle and firing accuracy properly in different surroundings of large airspace. In this paper, a new adaptive guidance strategy based on Back Propagation (BP) neural network for the reentry mission of a generic hypersonic vehicle is presented. Depending on the nicer self-learn ability of BP neural network, the guidance law considers the influence of biggish mis-modeling of aerodynamics, structure error and other initial disturbances on the flight capability of vehicle. Consequently, terminal position accuracy and velocity are guaranteed, while many constraints are satisfied. Numerical simulation results clearly bring out the fact that the proposed reentry guidance law based on BP neural network is rational and effective.

Citation Download Citation

Zongzhun Zheng, Yongji Wang, and Hao Wu "Study on adaptive BTT reentry speed depletion guidance law based on BP neural network", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881X (15 November 2007); https://doi.org/10.1117/12.750391

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