Computational Predictor–Corrector Homing Guidance for Constrained Impact

This paper proposes a computational predictor–corrector guidance algorithm for homing missiles to constrain the terminal impact angle and impact time. The guidance algorithm developed leverages a cascaded design philosophy to cater to the terminal constraints and considers the variation of the moving speed of the missile subject to both drag and gravity forces. The inner loop employs a deep neural network as a predictor to accurately forecast the impact angle, and a reinforcement learning algorithm serves as a corrector that generates a biased guidance command to eliminate the impact angle error. The outer loop uses a nested neural predictor–corrector that considers the inner loop command as the baseline guidance command to address the constraint on impact time. Extensive numerical simulation and several comparisons validate the performance of the proposed guidance algorithm in this paper.