A Learning-Based Computational Impact Time Guidance
This work addresses impact-time-control for guidance systems, presenting an incremental improvement by integrating deep learning and reinforcement learning to handle sparse rewards.
The paper tackles the problem of impact-time-control by proposing a learning-based computational guidance algorithm that combines deep neural networks and reinforcement learning to estimate time-to-go and correct impact time errors, achieving unspecified performance improvements in simulations.
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is developed based on a general prediction-correction concept: the exact time-to-go under proportional navigation guidance with realistic aerodynamic characteristics is estimated by a deep neural network and a biased command to nullify the impact time error is developed by utilizing the emerging reinforcement learning techniques. The deep neural network is augmented into the reinforcement learning block to resolve the issue of sparse reward that has been observed in typical reinforcement learning formulation. Extensive numerical simulations are conducted to support the proposed algorithm.