基于DDPG的综合化航电系统多分区任务分配优化方法OACSTPCD

The integrated modular avionics(IMA)system implements the integration of multiple avionics functions under a shared resource platform through a spatio-temporal partitioning mechanism.The merit of the task distribution method between partitions determines the overall effectiveness of the IMA system.An optimization method based on deep reinforcement learning(DRL)is proposed for the distribution and scheduling of avionics task sets within multiple partitions is proposed.The IMA system model and task model are constructed,and the constraints of system resource and task real-time requirements are used to improve the system resource utilization as the optimization objective.The task distribution process is described as a sequential decision problem.A Markov decision model is introduced to develop a deep deterministic policy gradient(DDPG)algorithm-based IMA task distribution model and a generic distribution architecture is proposed.Policy training techniques such as state normalization and behavioral noise are introduced to improve the learning performance and training capability of the DDPG algorithm.Simulation results show that the proposed optimization algorithm starts to converge after 500 iterations,and the efficiency of distribution scheme is improved by 20.55%while satisfying the constraint requirements after 800 iterations.Compared with the traditional assignment scheme and the Actor-Critic(AC)algorithm,the proposed DDPG algorithm has significant advantages in terms of convergence ability.