On-line suboptimal midcourse guidance using neural networks

This paper considers mideourse guidance of Air to-air tactical missiles where seeker lock-on is not Achieved at launch. An on-line suboptimal midcourse guidance law, which is a neural network approximation of optimal feedback strategy, is derived to eliminate the need for solving the two-point boundary value problems in real time. Since the proposed guidance law is an approximation, it is not very accurate but suitable for midcourse guidance. The approach is to teach the neural network to extract the information about the mapping in question from the off line generated optimal trajectories corresponding to various final conditions in the region where a target is predicted to appear and to use it as a feedback scheme for real-time on-board implementation. It needs an assumption that the optimal control signal can be expressed as a function of the states and final conditions.