OPTIMAL MIDCOURSE GUIDANCE LAW WITH NEURAL NETWORKS

A neural-network-based synthesis of an optimal midcourse guidance law is presented in this study. We use a set of two neural networks; the first network called a “critic” outputs the Lagrange's multipliers arising in an optimal control formulation and second network, called an “action” network, outputs the optimal guidance/control. The system equations, the optimality conditions, the costate equations are used in conjunction with the network outputs to provide the targets for the neural networks. When the critic and action network are mutually consistent, the output of the action network yields optimal guidance/control. Numerical results for a number of scenarios show that the network performance is excellent. Corroboration for optimality is provided by comparisons of the numerical solutions using a shooting method for a number of scenarios.