Rocket launcher system, as a special launcher placed on tactical vehicles, is a very complex mechanical system with characteristics of strong shock and vibration. In order to improve position accuracy, as well as reduce vibration, this paper creates a nonlinear dynamics model of the launcher system by using a new version of the transfer matrix method for multibody systems. The overall transfer equation of the nonlinear model is deduced. Combining with general kinematics equations of the rocket, the system launch dynamics are simulated and compared with experiments to verify the correctness of the model. On this basis, a backpropagation neural network proportional–integral–derivative adaptive control system is designed to improve servo control of the launcher. Then, the effectiveness of this method is verified by comparing with the traditional proportional–integral–derivative control method. Simulated results show that the backpropagation neural network proportional–integral–derivative control system makes the azimuth and elevation angles reach the target values smoothly and quickly, with higher accuracy. The results prove that the proposed method prominently reduces vibrations of the launcher, by adjusting the control parameters online according to the operation state of the system, presenting a better stability and robustness.

中文翻译：

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使用新版转移矩阵方法的多体系统特殊发射器反向传播神经网络比例-积分-导数的仿真和自适应控制

火箭发射器系统作为战术车辆上的专用发射器，是一种非常复杂的机械系统，具有强烈的冲击和振动特性。为了提高位置精度并减少振动，本文使用新版本的多体系统传递矩阵方法创建了发射器系统的非线性动力学模型。推导了非线性模型的整体传递方程。结合火箭的一般运动学方程，对系统发射动力学进行了仿真，并与实验进行了比较，以验证模型的正确性。在此基础上，设计了一种反向传播神经网络比例-积分-微分自适应控制系统，以改进发射器的伺服控制。然后，通过与传统的比例积分微分控制方法进行比较，验证了该方法的有效性。仿真结果表明，反向传播神经网络比例-积分-微分控制系统可以使方位角和仰角平稳，快速地达到目标值，并且精度更高。结果证明，该方法通过根据系统的运行状态在线调整控制参数，显着降低了发射器的振动，具有较好的稳定性和鲁棒性。