空间环境下惯性展开机构动态性能可靠性分析

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提出惯性展开机构运动参数动态可靠性分析方法，建立了惯性展开机构运动参数动态可靠性分析模型．将驱动力（矩）、摩擦和阻尼力（矩）等作为随机变量，应用蒙特卡罗方法，取得动态参数样本，再利用人工神经网络方法，用随机抽取的样本对网络进行训练，统计网络输出的动态参数分布，得到惯性展开机构动态可靠度．空间站惯性展开机构动态可靠度计算实例表明，该方法简单实用，计算成本低． Methodology of inertia expanding mechanism kinematical parameters dynamical reliability analysis was presented. A general model of kinematical parameters dynamical reliability was introduced for inertia expanding mechanism. Stochastic variables including driven forces, torques, frictions and damps were considered basically. First, Monte Carlo（MC） method was applied to generate stochastic variables and dynamical responds of mechanism. Then, the application of Artificial Neural Network（ANN） was motivated by the approximate concepts inherent in reliability analysis and time consuming repetition required for MC. Finally, statistical distribution of kinematical parameters was yielded from the outputs of ANN. As an example, a space station inertia expanding mechanism model was employed to test this method. The results proved that this method could be used to account for the complicated dynamical reliability analysis at a reasonable computational cost.