空间非合作目标惯性参数的Adaline网络辨识方法

doi:10.7527/S1000-6893.2015.0349

Abstract

Abstract:

During the operation in space, the spacecraft's attitude and trajectory are often affected by capturing the uncooperative target. In order to overcome the influence of uncooperative target on the dynamics and kinematics of spacecraft and ensure the high-precision attitude control strategy to be made and normal in-orbit condition, a process of identifying the inertial parameters of uncooperative targets should be accommodated. In order to avoid a large amount of computation induced by generalized inverse operation of traditional method in the identification process, which also causes severe vibration and unstability to numerical results, an Adaline neural network identification method based on normalized least mean square(NLMS) criterion is adopted. First of all, a system model composed of spacecraft, manipulator and uncooperative target is established based on the theory of momentum conservation. Then the weight parameters of the neural network representing the inertial parameters of uncooperative target are trained by the coefficient matrix of the identification equation as the input and output of the neural network via algorithm of NLMS with variable iterative step, and a fast and accurate process of identification is achieved. Finally, an ADAMS/MATLAB co-simulation platform is established, on which the proposed identification method is verified. The simulation results show that the Adaline neural network based on NLMS criterion is a fast and accurate method for identifying the target's inertia parameters.

Key words: spacecraft, uncooperative target, inertial parameter, neural network, identification

CLC Number:

V441

SUN Jun, ZHANG Shijie, MA Ye, CHU Zhongyi. Adaline network-based identification method of inertial parameters for space uncooperative targets[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016, 37(9): 2799-2808.

References

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Adaline network-based identification method of inertial parameters for space uncooperative targets

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