强不确定系统“全系数之和等于1”的实现方法

摘要： 研究强不确定系统“全系数之和等于1”的实现方法，强不确定系统指的是系统的静态增益及其界不完全确知且范围较大.“全系数之和等于1”是吴宏鑫院士20世纪80年代发现的，该原理表明，对于未知连续系统，其离散化系统的系数的和在一定条件下是1.该原理的发现对于解决闭环辨识和自适应控制的瓶颈问题具有关键作用.“全系数之和等于1”是在一定条件下成立的.为了实现系统的“全系数之和等于1”，需要对系统进行一定的变换，以满足所需条件.其中，采用静态增益的标称值的倒数进行输入变换的方法在实际中得到了广泛应用.但是，当系统的不确定性较大时，该变换将带来较大偏差.针对该问题开展了深入研究，明确给出了系统静态增益的不确定性与标称值的比值的关系对于实现“全系数之和等于1”的影响.当不确定性与标称值的比值较小时，可以近似实现“全系数之和等于1”；当比值较大时，进一步给出了通过选取合适的采样周期，近似实现“全系数之和等于1”的方法.本文的研究对于特征模型理论在实际中的应用提供了一定的基础.     

Global ionospheric scintillations revealed by GPS radio occultation data with FY3C satellite before midnight during the March 2015 storm

Global observations of S4 amplitude scintillation index by the GPS Occultation Sounder (GNOS) on FengYun-3 C (FY3C) satellite reveal global dynamic patterns of a strong pre-midnight scintillations in F-region of the ionosphere during the St. Patrick’s Day geomagnetic super storm of 17–19 March 2015. The observed strong scintillations mainly occurred in the low latitudes, caused by equatorial plasma bubbles. During the main storm phase (March 17), the scintillations were first triggered in the New Zealand sector near 160°E longitudes, extending beyond 40°S dip latitude. They were also enhanced in the Indian sector, but significantly suppressed in East Asia near 120°E longitude and in Africa around 30°E longitude. During the initial recovery phase (March 18–19), the global scintillations were seldom observed in GNOS data. During the later recovery phase (after March 19), the scintillations recovered to the pre-storm level in Indian, African, and American sectors, but not in East Asian and any of Pacific sectors. These results closely correlate with observations of the density depletion structures by the Communication/Navigation Outage Forecasting System (C/NOFS) satellite, and ground-based instruments. Such consistency indicates reliability of our scintillation sensing approach even in a case-by-case comparison study. The prompt penetration electric field and disturbance dynamo electric field are suggested as the main factors that control the enhancement and inhibition of the scintillations during the storm, respectively.     

Fault Estimation and Accommodation for a Class of Nonlinear System Based on Neural Network Observer

The problem of fault estimation and accommodation of nonlinear systems with disturbances is studied using adaptive observer and neural network techniques.A robust adaptive learning algorithm based on switchingβsmodification is developed to realize the accurate and fast estimation of unknown actuator faults or component faults.Then a fault tolerant controller is designed to restore system performance.Dynamic error convergence and system stability can be guaranteed by Lyapunov stability theory.Finally,simulation results of quadrotor helicopter attitude systems are presented to illustrate the efficiency of the proposed techniques.     

Ionospheric F2-Layer critical frequency retrieved from COSMIC radio occultation: A statistical comparison with measurements from a meridional ionosonde chain over Southeast Asia

In this paper, we compared the F2-Layer critical frequency (foF2) derived from FORMOSAT-3/COSMIC radio occultation (RO) and ionosondes at Chiang Mai, Chumphon and Kototabang during the years 2008–2015 to evaluate the performance of COSMIC RO over Southeast Asia region. The results show that the time development of foF2 values derived from COSMIC RO generally agrees well with those from ionosonde measurements. However, the differences between the foF2 derived from COSMIC RO and that derived from ionosonde observations display latitudinal dependence. COSMIC RO tends to underestimate foF2 at Chiang Mai and Kototabang, which is near to the north EIA crest and the south one, respectively, while a little overestimate foF2 at Chumphon, which is close to the geomagnetic equator. COSMIC RO agrees best with ionosonde at Chumphon and worst at Chiang Mai. At each ionosonde station, the quality of COSMIC RO data degrades with the increase of solar activity. In addition, at the station Chiang Mai and Kototabang, COSMIC RO performs better in summer than in equinox and winter. Furthermore, the differences in foF2 derived from COSMIC RO and that from ionosonde measurements vary with local time, i.e., the differences in foF2 are generally smaller at night and larger in noontime when equatorial ionization anomaly (EIA) is well developed.     

MSG-3在民用航空器维修大纲制定中的应用

国内民用航空器的研发起步较晚,目前投入运行的国产航空器型号种类和数量都不多,航空器制造厂家在制定维修大纲方面的经验较少.本文对国际上通行的MSG-3维修任务分析方法进行研究,首先介绍MSG-3维修任务分析方法产生和发展的历史背景,以及其在国内民机型号中的应用;然后详细叙述MSG-3维修任务分析方法(包括航空器系统、动力装置维修任务的分析方法,结构检查和区域检查任务的分析方法,以及形成维修大纲的流程和维修任务的后期优化);最后通过对航空器型号的审查经验,总结我国民用航空器制造厂家在制定航空器维修大纲时存在的问题和改进方向.     

航空发动机增益调度控制的多项式平方和规划方法

针对现有的线性变参数(linear parameter varying,LPV)控制器设计方法都是关于仿射参数依赖系统而没有专门针对多项式描述的LPV系统这一现状,提出了一种基于多项式平方和(sum of squares,SOS)规划的增益调度控制设计方法,并将其用于转速大范围变化时的航空发动机高压转子转速及压比控制.根据发动机非线性模型获取不同转速下的状态空间模型,并利用多项式拟合的方法建立发动机线性变参数模型.给出能够保证无静差的增益调度控制结构,利用有界实定理和多项式平方和理论推导出能够保证闭环系统鲁棒稳定的SOS约束条件,并形成控制器求解的SOS规划问题,通过求解获得多项式描述的增益调度控制器.分别以LPV模型和发动机非线性模型为对象做阶跃仿真,结果表明:高压转子转速/发动机压比控制系统的调节时间在2s以内,稳态误差不超过0.1%.      

基于Kalman滤波的变体飞行器T-S模糊控制

针对变体飞行器的跟踪控制问题,提出了一种基于Kalman滤波的T-S模糊控制方法。考虑飞行器系统状态不可测,引入惯导数据作为辅助信息,利用Kalman滤波算法融合飞控信息与惯导信息实现状态估计。由于变体飞行器在不同变形结构下气动特性变化较大,为便于控制器设计,采用小扰动线性化方法得到飞行器在不同平衡点处的局部线性模型,并通过状态反馈方法设计局部控制器,局部线性模型和局部控制器通过模糊集和模糊规则聚合成一个连续光滑的全局T-S模糊模型和T-S模糊控制器。通过综合Kalman滤波器与T-S模糊控制器得到一个基于Kalman滤波的T-S模糊控制器。仿真结果表明,该控制器在变形过程中能够实现状态估计,保证飞机的跟踪性能。     

流场可压缩性对涡相互作用影响的数值研究

涡相互作用作为冲压发动机喷注装置的典型抽象流动现象,研究其可压缩性影响对于认识包含化学反应的真实燃料喷射场具有一定基础理论价值。基于经过数值验证的可压缩Navier-Stokes算法与压力泊松方程初始条件设置方法研究流场可压缩性对涡相互作用演化过程的影响。结果表明,以较高涡旋马赫数表征的可压缩性在改变涡对形态的同时,具有延缓涡旋相互靠近,迟滞融合进程的作用。关于涡对系统开始融合的临界条件,可压缩相互作用开始的临界展弦比与无量纲时间相比于低速涡对明显提高。为在无量纲时间意义下统一不同马赫数涡对相互作用的进程,从涡心密度随时间变化规律出发在不可压涡对特征时间的基础上,初步构建了考虑可压缩性的时间尺度修正关系。     

战斗机嵌入式训练系统中的智能虚拟陪练

智能化"实虚"对抗是现代先进战斗机嵌入式训练系统的重要功能需求。自主空战决策控制技术在未来空战装备发展中扮演关键角色。将当前的功能需求和发展中的技术结合起来,得到了空战智能虚拟陪练的概念。先进控制决策技术的引入使得智能虚拟陪练能够帮助飞行员完成复杂的战术训练,而训练中真实的对抗场景为技术的验证提供了理想的环境,大量的训练数据为技术的持续迭代优化提供了保障。作为可学习和进化的空战战术专家,智能陪练在人机对抗和自我对抗中不断优化,当其具备与人相当甚至超越人的战术能力时,可应用于未来的无人空战系统。智能虚拟陪练需要具备4项基本能力：智能决策能力、知识学习能力、对抗自优化能力和参数化表示能力。对其包含的关键技术进行了分析,提出并实现了一个基于模糊推理、神经网络和强化学习的解决方案,展示了其各项基本能力及目前达到的空战水平。未来更多的模型和算法可在智能虚拟陪练的框架中进行验证和优化。