In this paper, a deep neural network-based finite-time input-to-state stability (ISS) terminal guidance law (DNN-FTG) is proposed, which uses neural network to replace the traditional finite-time ISS terminal guidance law to improve the guidance performance against maneuvering targets. The guidance law uses a large amount of simulation data from the finite-time ISS guidance law to train the deep neural network. Then, DNN-FTG is simulated and compared with the finite-time ISS guidance law. The guidance performance of DNN-FTG is evaluated by miss distance and energy consumption, and is further verified by Monte Carlo simulations, under different initial conditions, maneuvering forms, and anti-interference tests. The results show that DNN-FTG can replace the finite-time ISS guidance law for real-time guidance with better performance, generalization capability, and robustness.
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