The task of intercepting a target moving along a rectilinear or circular trajectory by
a Dubins’ car is formulated as a problem of time-optimal control with an arbitrary direction
of the car’s velocity at the time of interception. To solve this problem and to synthesize
interception trajectories, neural network methods of unsupervised learning based on the Deep
Deterministic Policy Gradient algorithm are used. The analysis of the obtained control laws
and interception trajectories is carried out in comparison with the analytical solutions of the
interception problem. Mathematical modeling of the target motion parameters, which the
neural network had not previously seen during training, is carried out. Model experiments are
conducted to test the stability of the neural solution. The effectiveness of using neural network
methods for the synthesis of interception trajectories for given classes of target movements is
shown.
