Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.     

基于智能差分算法的摩擦力补偿方法研究

针对光电平台低速转动时，受摩擦力影响较大，使得速度跟随曲线出现“死区”现象，导致跟踪性能明显下降这一问题，提出了一种基于智能差分进化算法和Lurge摩擦模型的摩擦力补偿控制方法。通过采集记录光电转台正、反向匀速运动时的摩擦力大小，建立转台不同速度和摩擦力之间的对应关系。通过最小二乘法对摩擦模型静态参数进行分段拟合，采用智能差分进化算法辨识摩擦模型动态参数，并基于反馈的速度信息和获得的摩擦模型等效为摩擦补偿力矩输入到电流环控制输入端，实现平台平稳低速运行。实验结果表明：摩擦力补偿后速度响应误差由补偿前的±0.1°/s减小到±0.04 °/s，提出方法效果显著。