面向非沿轨迹成像的切比雪夫神经网络滑模姿态控制

doi:10.7527/S1000-6893.2015.0114

Abstract

Abstract:

A nonsingular and fast terminal sliding mode controller based on the Chebyshev neural network (CNN) is designed for nonparallel-ground-track imaging mode, whose ground targets and ground track are not parallel. Firstly, the specific method of attitude adjustment for nonparallel-ground-track imaging mode is studied to get the desired attitude angle and angular velocity. Secondly, according to the tracking dynamic and kinematic model described by quaternion error, a nonsingular and fast terminal sliding mode (NFTSM) controller is derived. In order to enhance control accuracy, a CNN that is implemented using only desired signals is introduced to approximate the general disturbance which efficiently weakens the chattering inherent in sliding mode structure. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated disturbance, a switch function is applied to generating a switching between the adaptive neural network (ANN) control and the robust controller. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of environmental disturbance and parameters' uncertainties are performed, the results of which show that the designed control scheme has fast convergence, high control accuracy and certain actual engineering significance.

Key words: remote sensing, attitude tracking, sliding mode control, Chebyshev neural network (CNN), Lyapunov method

CLC Number:

V448.22

YE Dong, TU Yuanyuan, SUN Zhaowei. Sliding mode control for nonparallel-ground-track imaging using Chebyshev neural network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(9): 3092-3104.

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Sliding mode control for nonparallel-ground-track imaging using Chebyshev neural network

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