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.