Bipartite graph-based control flow checking for COTS-based small satellites

Single event upset(SEU)effect,caused by highly energized particles in aerospace,threatens the reliability and security of small satellites composed of commercial-off-the-shelves(COTS).SEUinduced control flow errors(CFEs)may cause unpredictable behavior or crashes of COTS-based small satellites.This paper proposes a generic software-based control flow checking technique(CFC)and bipartite graph-based control flow checking(BGCFC).To simplify the types of illegal branches,it transforms the conventional control flow graph into the equivalent bipartite graph.It checks the legality of control flow at runtime by comparing a global signature with the expected value and introduces consecutive IDs and bitmaps to reduce the time and memory overhead.Theoretical analysis shows that BGCFC can detect all types of inter-node CFEs with constant time and memory overhead.Practical tests verify the result of theoretical analysis.Compared with previous techniques,BGCFC achieves the highest error detection rate,lower time and memory overhead;the composite result in evaluation factor shows that BGCFC is the most effective one among all these techniques.The results in both theory and practice verify the applicability of BGCFC for COTS-based small satellites.     

基于拉格朗日拟序结构的局部激励流动分离控制有效性分析方法

从流体输运角度提出了用于局部周期激励流动分离控制有效性研究的数值分析方法。基于有限时间不变流形理论建立用于非定常流动的流体输运分析方法,并采用数值方法从非定常流场中提取得到吸引拉格朗日拟序结构（LCSs）和排斥LCSs描述流体输运行为。通过对局部周期激励的流动分离控制规律的研究,结果表明存在三种与激励频率相关的影响翼型气动特性的流体输运模式,其中在锁频激励下吸引LCSs在前缘形成的尖楔结构有效增强主流与分离区流体的物质交换,减小翼面流动分离区的面积,显著提高翼型升力。     

基于RVM回归误差补偿的航空发动机分布式控制系统多步预测控制

针对具有随机有界双侧时延的航空发动机分布式控制系统,提出了一种基于多步预测和关联向量机(RVM)回归误差补偿的控制方案.首先建立航空发动机分布式控制系统(DCS)的神经网络非线性自回归滑动平均(NARMA)模型,利用当前的系统输出和控制量对N步之后的系统输出进行预测;其次用改进的RVM回归多步预测算法估计NARMA模型的的预测误差,并对预测结果进行误差补偿;最后利用补偿之后的预测值和设定值对控制参数进行滚动优化,设计系统的神经网络逆控制器实现系统的自适应控制.仿真结果证明该控制策略能够避免随机有界双侧时延对控制系统的影响,实现对设定值的稳定跟踪,且控制器具有较好的实时性和鲁棒性.低压转子转速阶跃响应的稳态绝对误差小于0.04%,响应时间小于0.3s.