Abstract: In this paper, a new impact angle constrained guidance law with deep reinforcement learning and time to go aware logistic sigmoid function is proposed for varying velocity missile with partial actuator failure against a maneuvering target in the atmosphere. With model based deep reinforcement learning, a deep neural network is trained as a deep neural dynamics model to be used in model predictive path integral control. Partial actuator and target maneuver will make significant change to environment during guidance, thus the deep neural dynamics is trained to adapt to these changes online via meta learning to tackle this problem. The deep neural dynamics is then utilized through model predictive path integral control to achieve the guidance design. To benefit the sampling efficiency in model predictive path integral control, a novel sampling method using skew normal distribution is proposed in this work. Furthermore, a time to go aware logistic function is designed in the performance index to enhance guidance performance through reduced initial acceleration command and increased terminal velocity. Numerical simulations under various condition and Monte Carlo simulation demonstrate the effectiveness of the proposed guidance law.

Key words: Missile, Impact angle constraint, Deep reinforcement meta learning, Fault tolerant control, Guidance