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针对升力式再入飞行器的再入制导问题,根据设计得到的参考阻力加速度剖面,对动态逆阻力加速度跟踪原理进行分析,利用单隐层神经网络良好的非线性逼近能力,采用在线神经网络对逆误差进行补偿,并考虑气动参数偏差情况下的跟踪效果进行对比。针对参考阻力加速度变化剧烈而导致控制输入饱和的问题,本文引入PCH(Pseudo-control Hedging)思想,通过修正参考模型把参考信号限制在控制输入可实现的范围之内。仿真结果表明,该方法跟踪精度较高,可一定程度上缓解控制输入饱和。
Aiming at the reentry guidance problem of lift reentry vehicle, according to the designed reference resistance acceleration profile, the dynamic anti-drag acceleration tracking principle is analyzed. Based on the good non-linear approximation capability of single hidden layer neural network, Error compensation, and take into account the deviation of the aerodynamic parameters of the tracking effect comparison. Aiming at the problem that the control input saturation is caused by the drastic change of the reference resistance acceleration, the idea of PCH (Pseudo-control Hedging) is introduced in this paper. The reference signal is modified to limit the reference signal within the controllable input range. The simulation results show that the proposed method can track the input saturation accurately to a certain extent.