A Deep Q-Network-Based Collaborative Control Research for Smart Ammunition Formation

The smart ammunition formation (SAF) system model usually has the characteristics of complexity, time variation, and nonlinearity. With the consideration of random factors, such as sensor error and environmental disturbance, the system model cannot be modeled accurately. To deal with this problem, this paper investigated an intelligent deep Q-network- (DQN-) based control algorithm for the SAF collaborative control, which deals with the high dynamics and uncertainty in the SAF flight environment. In the environment description of the SAF, we built a dynamic model to represent the system joint states, which referred to the smart ammunition's velocity, the trajectory inclination angle, the ballistic deflection angle, and the relative position between different formation nodes. Next, we describe the SAF collaborative control process as a Markov decision process (MDP) with the application of the reinforcement learning (RL) technique. Then, the basic framework e-imitation action-selecting strategy and the algorithm details were developed to address the SAF control problem based on the DQN scheme. Finally, the numerical simulation was carried out to verify the effectiveness and portability of the DQN-based algorithm. The average total reward curve showed a reasonable convergence, and the relative kinematic relationship among the formation nodes met the requirements of the controller design. It illustrated that the DQN-based algorithm obtained a novel performance in the SAF collaborative control.

Automated summary

This paper investigates an intelligent deep Q-network (DQN) based control algorithm for the smart ammunition formation (SAF) collaborative control in a high dynamic and uncertain flight environment. The algorithm is developed with a dynamic model and reinforcement learning technique, showing novel performance in the SAF control.