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Proceedings Paper

Adaptive neural network nonlinear control for BTT missile based on the differential geometry method
Author(s): Hao Wu; Yongji Wang; Jiangsheng Xu
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Paper Abstract

A new nonlinear control strategy incorporated the differential geometry method with adaptive neural networks is presented for the nonlinear coupling system of Bank-to-Turn missile in reentry phase. The basic control law is designed using the differential geometry feedback linearization method, and the online learning neural networks are used to compensate the system errors due to aerodynamic parameter errors and external disturbance in view of the arbitrary nonlinear mapping and rapid online learning ability for multi-layer neural networks. The online weights and thresholds tuning rules are deduced according to the tracking error performance functions by Levenberg-Marquardt algorithm, which will make the learning process faster and more stable. The six degree of freedom simulation results show that the attitude angles can track the desired trajectory precisely. It means that the proposed strategy effectively enhance the stability, the tracking performance and the robustness of the control system.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678822 (15 November 2007); doi: 10.1117/12.750634
Show Author Affiliations
Hao Wu, Huazhong Univ. of Science and Technology (China)
Key Lab. of Ministry of Education for Image Processing and Intelligent Control (China)
Yongji Wang, Huazhong Univ. of Science and Technology (China)
Key Lab. of Ministry of Education for Image Processing and Intelligent Control (China)
Jiangsheng Xu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision

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