神经网络最优闭环制导律设计

对于复杂的非线性导弹制导系统 ,很难求得其解析的最优制导律 ,只能求得开环的数字解 ,不能适用于具有时变不确定性的导弹制导系统。利用神经网络的学习和推广能力 ,对开环的数字最优制导律进行离线的学习 ,作为闭环的神经最优制导律在线应用。研究分别选择系统状态变量和视线角速率等不同的神经网络输入对制导系统性能的影响 ,以及各种制导律的鲁棒性问题 ,并采用模块化神经网络结构提高神经网络的学习和推广能力 ,仿真结果得到一些有益的结论。

Design of Closed Loop Optimal Guidance Law Using Neural Networks

It is generally impossible to obtain the analytic optimal guidance law for complex nonlinear guidance systems of homing missiles,and the open loop optimal guidance law is often obtained by numerical methods,which can not be used directly in practice.The neural networks are trained off line using the optimal trajectory of the missile produced by the numerical open loop optimal guidance law,and then, the converged neural networks are used on line as the feedback optimal guidance law in real time.The research shows that different selections of the neural networks inputs,such as the system state variables or the rate of LOS(line of sight),may have great effect on the performances of the guidance systems for homing missiles.The robustness for several guidance laws is investigated by simulations,and the modular neural networks architectures are used to increase the approximating and generalizing abilities in the large state space.Some useful conclusions are obtained by simulation results. \{