Abstract:

Aiming at the problem of slow training speed and poor convergence of deep reinforcement learning method for the autopilot control parameters training, an intelligent training method that converts three-dimensional control parameters into one-dimensional design parameters is proposed with the three-loop autopilot pole placement method as the core. The intelligent control architecture of offline deep reinforcement learning training and online multi-layer perceptron neural network real-time calculation is constructed, which improves the efficiency and convergence of deep reinforcement learning algorithm and ensures the rapid online tuning of control parameters under the condition of large-scale flight state changes. Taking a typical reentry aircraft as an example, the deep reinforcement learning training and neural network deployment are accomplished. The simulation results show that the training efficiency of the simplified reinforcement learning action space is higher, and the tracking error of the controller to the control command is less than 1.2% by the proposed parameter rapid tuning method based on deep reinforcement learning.

Key words: reinforcement learning, autopilot, parameter tuning, intelligent control, normalization