Automatic Generation of Reconfiguration Blueprints for IMA Systems Using Reinforcement Learning

Abstract

Reconfiguration is a crucial method to improve the fault-tolerant capability of the integrated modular avionics (IMA) system. The quality of the blueprints has a substantial influence on operational performance after the system reconfiguration. Hence, this letter proposes an intelligent reconfiguration algorithm, which applies reinforcement learning (RL) to the IMA system. The proposed algorithm combines the critical elements of the RL model with the established IMA reconfiguration model to generate low-cost and high-quality blueprints. The experiment results demonstrate that the proposed method can not only generate feasible blueprints rapidly but also enhance system performance by optimizing load balance and reconfiguration costs.