Intelligent Soft Computing-Based Security Control for Energy Management Architecture of Hybrid Emergency Power System for More-Electric Aircrafts

This paper proposes an attack-resilient intelligent soft computing-based security control for energy management architecture for a hybrid emergency power system of more-electric aircrafts (MEAs). Our proposed architecture develops an adaptive neuro-fuzzy inference system (ANFIS)-based method in conjunction with a specific recurrent neural network architecture called long-short-term memory (LSTM) to evaluate the integrity of the power output of the critical components, which have higher vulnerability to the potential cyber-attacks and critical for the effective energy management and emergency control. We evaluate the integrity of the critical measurements that have high vulnerability to the potential cyber-attacks by using LSTM techniques. After identifying the compromised measurements, we continue to activate the closed-loop ANFIS mechanism in which the structure of ANFIS is controlled according to the accuracy of its current estimations by probing the physical couplings in the system. In our simulation, we evaluate the performance of our proposed LSTM-ANFIS -based method to support the integrity of the energy management strategies used in hybrid emergency power system for MEAs by using TensorFlow and MATLAB/Simulink co-simulation environment. Our simulation results illustrate the effectiveness of our proposed method in effectively evaluating the integrity of critical data and achieving resilient control.