空间飞行器导引律优化控制三维仿真

Three Dimensional Simulation of Space Vehicle Guidance Law Optimization Control

由于飞行器导引控制是一个多输入多输出强耦合的非线性系统,随着目标机动能力的增强,不仅要求能准确估计目标的机动,更需要研究三维制导模型下的鲁棒性强的导引方法。为此应用神经网络动态面控制方法设计了一种新型三维空间导引律。在导引律的设计过程中,通过引入一阶低通滤波器,使得导引律的最终表达形式中不含有中间变量的高阶导数,更易于实际应用。通过采用RBF神经网络逼近相对运动模型中的耦合项和目标运动带来的干扰项,无须建立机动模型,估计精度高。应用Layapunov稳定性判据对其稳定性进行了证明。仿真结果表明,在目标复杂机动的情况下,采用上述导引律的飞行器能够获得较小的脱靶量,分析证明了优化控制方法的有效性。

The guidance and control of space vehicle is a MIMO and nonlinear system with strong coupling. As the enhance of target maneuver ability,it is not only required to be able to accurately estimate the target maneuvering,but also need to study the robust guidance method under 3D guidance model. Based on neural dynamic surface control,a new three- dimensional（ 3D） guidance law was proposed in the paper. In the design of the guidance law,certain first- order low- pass filters were introduced to avoid the occurrence of high- order derivatives of intermediate variables in the expression of the guidance law,which makes it easy to implement in practical applications. RBF neural networks were used to approximate the coupling terms in the relative motion model and the interference terms which the motions of target brings,which makes a simple form of the guidance law. Finally,Layapunov stability criterion was used to prove the stability of the guidance law. Simulations show that the vehicle which uses this guidance law can obtain smaller miss distance in the case of complicated maneuvering target and the analysis proves the effectiveness of the optimal control method.