Abstract:

A robust guidance law for homing missile is designed and optimized using neural networks. The nonlinear kinematics and robust performance of the guidance system are presented, and then, the robust performance is equated to a min max problem of the differential games. It makes the solving of a two points boundary value problem of differential games into the training of two neural networks by using the adjoint techniques of optimal control and backpropagation techniques of neural networks. When neural networks are converged, the two neural networks can be used as the optimal differential games controllers on line, avoiding solving the complex robust missile guidance law problem directly. The sensitivity to initial states in solving optimal controller can be avoided to some extent by making the changes of initial states into the robust performance or by learning on differential initial states using neural networks. The simulation results show the effectiveness of the method.

Key words: differential games, neural networks, missile guidance, robust control