Optimal feedback missile guidance law using neural networks

It is general impossible to obtain the analytic optimal guidance law for the complex nonlinear guidance systems of the 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 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 the 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 are investigated by simulations. Some useful conclusions are obtained by simulation results.