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Proceedings Paper

A wavelet neural network based on genetic algorithm and its application to gain scheduling flight control
Author(s): Xun Sun; Weiguo Zhang; Wei Yin; Aijun Li
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Paper Abstract

As enlarging of the flight envelop, the aerodynamic derivative of the airplane varies enormous. The gain scheduling method is usually used to deal with it. But the workload is enormously and the stability is difficulty to be assured. To solve the above problem, a large envelope wavelet neural network gain scheduling flight control law design method based on genetic algorithm is presented in this paper. Wavelet has good time accuracy in high frequency-domain and the good frequency accuracy in low frequency-domain. Neural network has the self-learning character. In this method, wavelet function instead of Sigmoid function as the excitation function. So the two merits are merged and the high nonlinear function approximation capability could be achieved. In order to obtain higher accuracy and faster speed, genetic algorithm is used to optimize the parameters of the wavelet neural network. This method is used in design the large envelope gain scheduling flight control law. This simulation results show that good control capability could be achieved in large envelope and the system is still stable when modeling error is 20%. In the situation of 20% modeling error, the maximum overshoot is only 12m and it is 35% of the maximum overshoot using normal method.

Paper Details

Date Published: 30 October 2006
PDF: 6 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63582J (30 October 2006); doi: 10.1117/12.717965
Show Author Affiliations
Xun Sun, Northwestern Polytechnical Univ. (China)
Weiguo Zhang, Northwestern Polytechnical Univ. (China)
Wei Yin, Northwestern Polytechnical Univ. (China)
Aijun Li, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Jiancheng Fang; Zhongyu Wang, Editor(s)

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