  In this paper, we present a novel guidance scheme based on model-based deep
reinforcement learning (RL) technique. With model-based deep RL method, a deep
neural network is trained as a predictive model of guidance dynamics which is
incorporated into a model predictive path integral (MPPI) control framework.
However the traditional MPPI framework assumes the actual environment similar
to the training dataset for the deep neural network which is impractical in
practice with different maneuvering of target, other perturbations and actuator
failures. To address this problem, our method utilize meta-learning technique
to make the deep neural dynamics model adapt to such changes online. With this
approach we can alleviate the performance deterioration of standard MPPI
control caused by the difference between actual environment and training data.
Then, a novel guidance law for a varying velocity interceptor intercepting
maneuvering target with desired terminal impact angle under actuator failure is
constructed based on aforementioned techniques. Simulation and experiment
results under different cases show the effectiveness and robustness of the
proposed guidance law in achieving successful interceptions of maneuvering
target.
