电控单元电磁脉冲冗余优化分配策略

doi:10.16180/j.cnki.issn1007-7820.2022.08.005

摘要/Abstract

摘要：

针对控制单元冗余分配问题,文中提出了量子混沌自适应粒子群优化算法和热温备份混合策略。将电控单元简化成串并联系统,根据电磁脉冲下出现共因失效的情况,引入β因子,建立了电控单元在电磁脉冲下以最高可靠度为目标函数和以冗余数目为约束条件的Markov模型。利用量子混沌自适应粒子群优化算法对电控单元在电磁脉冲下最大可靠度进行优化。仿真结果表明,混合策略和优化算法提高了计算效率及冗余分配精确度,为电控单元在电磁脉冲下的冗余加固提供了参考依据。

关键词: 电磁脉冲, 电控单元, 冗余分配, 优化算法, 可靠性, 电磁仿生学, 热温备份策略, 马尔科夫模型

Abstract:

In order to solve the redundancy allocation problem in the control unit, a quantum chaotic adaptive particle swarm optimization algorithm and a hybrid strategy of thermal backup are proposed. According to the common cause failure under the electromagnetic pulse, electronically controlled unit is simplified into a series and parallel system, and β factor is introduced to build a Markov model with the highest reliability as the objective function and the redundancy number as the constraint condition. The quantum chaos adaptive particle swarm optimization algorithm is used to optimize the maximum reliability of electronically controlled unit under electromagnetic pulse. The simulation results show that the hybrid strategy and optimization algorithm improve the calculation efficiency and the accuracy of redundancy allocation, which provides a reference basis for the redundancy reinforcement of electronically controlled unit under electromagnetic pulse.

Key words: EMP, ECU, redundancy allocation, optimization algorithm, reliability, electromagnetic bionics, hot and warm backup strategy, Markov model

中图分类号:

TP302

季桢杰,魏民祥,查曰珩,周东,曹杰. 电控单元电磁脉冲冗余优化分配策略[J]. 电子科技, 2022, 35(8): 27-33.

JI Zhenjie,WEI Minxiang,ZHA Yueheng,ZHOU Dong,CAO Jie. Optimization Allocation Strategy of ECU Redundancy under EMP[J]. Electronic Science and Technology, 2022, 35(8): 27-33.

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模块名称	λ_i	λ_im
AD模块	0.007	0.000 1
MCU模块	0.004	0.000 1
通讯模块	0.001	0.000 1

算法	冗余分配数	整型	最佳可靠度
热备份上限	(1.73,2.50,0.50)	(2,3,1)	0.729
热备份下限	(1.68,2.50,0.53)	(2,3,1)	0.680
热温备份上限	(0.14,2.93,0.00, 0.66,1.54,1.75)	(0,3,0,1,2,2)	0.897
热温备份下限	(0.00,2.94,0.00, 0.93,1.57,1.72)	(0,3,0,1,2,2)	0.888

算法	冗余分配数	整型	最优解迭代数	最佳可靠度
PSO上限	(0.14,2.93,0.00, 0.66,1.54,1.75)	(0,3,0,1,2,2)	60	0.897
PSO下限	(0.00,2.94,0.00, 0.93,1.57,1.72)	(0,3,0,1,2,2)	44	0.888
优化算法上限	(0.11,2.81,0.01, 1.60,1.13,0.18)	(0,3,0,2,1,0)	3	0.899
优化算法下限	(0.00,2.55,0.33, 1.66,1.12,0.12)	(0,3,0,2,1,0)	3	0.891