一类滞后不确定相似组合系统的分散控制

本文给出了一个使滞后的相似组合大系统镇定的分散鲁棒控制器的设计方法。不确定性是时变的,是以不确定参数出现在每个子系统中,而存在于互联项中的不确定性假设可能知道它的边界,当系统具有某种特征时,简化了一般控制器的设计。     

基于自由权矩阵的时变时延线性群系统编队控制

研究了基于自由权矩阵方法的时变时延线性群系统编队控制问题。首先,基于一致性理论,设计了具有时变时延的线性群系统编队控制协议。其次,通过变量代换,将时延条件下的线性群系统编队控制问题转化为时延系统的镇定问题。构造Lyapunov-Krasovskii函数,并利用自由权矩阵方法对时延系统的镇定问题进行分析,得到了保守性较小的线性矩阵不等式（LMI）判据,并求解出时延上界和编队控制器增益。最后,通过仿真实验,验证了方法的有效性。     

数据融合的对流层天顶延迟估计方法

为提高对流层天顶延迟(Zenith tropospheric delay，ZTD)的估计性能，提出了基于数据融合的ZTD估计方法。估计干延迟采用Saastamoinen模型，估计湿延迟采用Askne模型，地表气象测量设备提供给两模型所需的气压、温度以及水汽压，Askne模型所需的加权温度、温度变化率和湿度变化率由全球气压和温度2w(Global pressure and temperature 2w，GPT2w)模型提供。当气象测量设备不可用时，上述所有气象参数均来自于GPT2w模型。利用国际GPS服务(International GPS service, IGS)提供的数据进行验证，结果表明：当地表测量设备存在时，所提方法较Saastamoinen模型提高了8mm；当全部气象参数来自GPT2w时，本方法较GPT2w+Saastamoinen模型提高了8.1mm；对于季节分明的测站，误差趋势同样具备季节性；对于海拔高和气候干燥的的测站，估计误差较小。     

Mitigation of receiver biases in ionospheric observables from PPP with ambiguity resolution

PPP (Precise Point Positioning) is a GNSS (Global Navigation Satellite Systems) positioning method that requires SSR (State Space Representation) corrections in order to provide solutions with an accuracy of centimetric level. The so-called RT-PPP (Real-time PPP) is possible thanks to real-time precise SSR products, for orbits and clocks, provided by IGS (International GNSS Service) and its associate analysis centers such as CNES (Centre National d'Etudes Spatiales). CNES SSR products also enable RT-PPP with integer ambiguity resolution. In GNSS related literature, PPP with ambiguity resolution (PPP-AR) in real-time is often referred as PPP-RTK (PPP – Real Time Kinematic). PPP-WIZARD (PPP - With Integer and Zero-difference Ambiguity Resolution Demonstrator) is a software that is made available by CNES. This software is capable of performing PPP-RTK. It estimates slant ionospheric delays and other GNSS positioning parameters. Since ionospheric effects are spatially correlated by GNSS data from active networks, it is possible to model and provide ionospheric delays for any position in the network coverage area. The prior knowledge ionospheric delays can reduce positioning convergence for PPP-RTK users. Real-time ionospheric models could benefit from highly precise ionospheric delays estimated in PPP-AR. In this study, we demonstrate that ionospheric delays obtained throughout PPP-AR estimation are actu ally ionospheric observables. Ionospheric observables are biased by an order of few meters caused by the receiver hardware biases. These biases prohibit the use of PPP-WIZARD ionospheric delays to produce ionospheric models. Receiver biases correction is essential to provide ionospheric delays while using PPP-AR based ionospheric observables. In this contribution, a method was implemented to estimate and mitigate receiver hardware biases influence on slant ionospheric observables from PPP-AR. In order to assess the proposed approach, PPP-AR data from 12 GNSS stations were processed over a two-month period (March and April 2018). A comparison between IGS ionospheric products and PPP-AR based ionospheric observables corrected for receiver biases, resulted in a mean of differences of −39 cm and 51 cm standard deviation. The results are consistent with the accuracy of the IGS ionospheric products, 2–8 TECU, considering that 1 TECU is ~16 cm in L1. In another analysis, a comparison of ionospheric delays from 5 pairs of short baselines GNSS stations found an agreement of 0.001 m in mean differences with 22 cm standard deviation after receiver biases were corrected. Therefore, the proposed solution is promising and could produce high quality (1–2 TECU) slant ionospheric delays. This product can be used in a large variety of modeling approaches, since ionospheric delays after correction are unbiased. These results indicate that the proposed strategy is promising, and could benefit applications that require accuracy of 1–2 TECU (~16–32 cm in L1).     

An Integer Precise Point Positioning technique for sea surface observations using a GPS buoy

GPS data dedicated to sea surface observation are usually processed using differential techniques. Unfortunately, the precision of resulting kinematic positions is baseline-length dependent. So, high precision sea surface observations using differential GPS techniques are limited to coasts, lakes, and rivers. Recent improvements in GPS satellite products (orbits, clocks, and phase biases) make phase ambiguity fixing at the zero difference level achievable and opens up the observation of the sea surface without geographical constraints. This paper recalls the concept of the Integer Precise Point Positioning technique and discusses the precision of GPS buoy positioning. A sequential version of the GINS software has been implemented to achieve single epoch GPS positioning. We used 1 Hz data from a two week GPS campaign conducted in the Kerguelen Islands. A GPS buoy has been moored close to a radar gauge and 90 m away from a permanent GPS station. This infrastructure offers the opportunity to compare both kinematic Integer Precise Point Positioning and classical differential GPS positioning techniques to in situ radar gauge data. We found that Precise Point Positioning results are not significantly biased with respect to radar gauge data and that horizontal time series are consistent with differential processing at the sub-centimetre precision level. Nevertheless, standard deviations of height time series with respect to radar gauge data are typically [4–5] cm. The dominant driver for noise at this level is attributed to errors in tropospheric estimates which propagate into position solutions.     

Fronts and thermohaline structure of the Brazil–Malvinas Confluence System

Fronts and thermohaline structure of the Brazil–Malvinas Confluence System (BMCS) are studied from climatic data, “Marathon Exp. Leg.8, 1984” data, and Sea surface temperature (SST) data base “ds277-Reynolds” (1981–2000). The South Atlantic Central Water (SACW) is divided in two main types: tropical (TW) and subtropical water (ST). Water masses, Fronts, Inter-Frontal and Frontal Zones are analysed and classified: (a) the water masses: Tropical Low-Salinity Water, Tropical Surface Water, Tropical Tropospheric Water, Subtropical Low-Salinity Water, Subtropical Surface Water, Subtropical Tropospheric Water. T,S characteristics of intermediate, deep and bottom water defined by different authors are confirmed and completed; (b) the Inter-Frontal Zones: Tropical/Brazil Current Zone, Subtropical Zone and Subantarctic Zone; (c) the Frontal Zones: Subtropical, Subantarctic and Polar, and (d) the Fronts: Subtropical Front of the Brazil Current, Principal Subtropical Front, North Subtropical Front, Subtropical Surface Front, South Subtropical Front, Subantarctic Surface Front, Subantarctic Front and Polar Front. Several stable T–S relationships are found below the friction layer and at the Fronts. The maximum gradient of the oceanographic characteristics occurs at the Brazil Current Front, which can be any of the subtropical Fronts, depending on season. Minimum mean depth of the pycnocline coincides with the Fronts of the BMCS, indicating the paths of low-salinity shelf waters into the open ocean. In the work it is shown how to recover the horizontal and vertical thermohaline structure of waters from satellite data RSMAS SST.     

Decentralized attitude synchronization tracking control for multiple spacecraft under directed communication topology

This paper studies the attitude synchronization tracking control of spacecraft formation flying with a directed communication topology and presents three different controllers. By introducing a novel error variable associated with rotation matrix, a decentralized attitude synchronization controller, which could obtain almost global asymptotical stability of the closed-loop system, is developed. Then, considering model uncertainties and unknown external disturbances, we propose a robust adaptive attitude synchronization controller by designing adaptive laws to estimate the unknown parameters. After that, the third controller is proposed by extending this method to the case of time-varying communication delays via Lyapunov–Krasovskii analysis. The distinctive feature of this work is to address attitude coordinated control with model uncertainties, unknown disturbances and time-varying delays in a decentralized framework, with a strongly connected directed information flow. It is shown that tracking and synchronization of an arbitrary desired attitude can be achieved when the stability condition is satisfied. Simulation results are provided to demonstrate the effectiveness of the proposed control schemes.     

基于贝叶斯网络的航班延误与风险共因性研究

在民航运输生产中,航班延误时常发生,不仅打乱乘客的出行计划,影响航空公司的信誉,同时也会成为民航安全风险的隐患,而民航安全风险发生的同时也常常会导致航班延误。构建航班延误与安全风险的共因性指标体系来分析两者的共因性,并建立了相应的贝叶斯网络模型。研究结果为分析航班延误与安全分析的共因性关联强度提供参考,并在一定程度上对航班延误与安全风险进行预警,确定控制和消除航班延误隐患的措施,提高民航安全。     

基于增量式排列支持向量机的机场航班延误预警

目前航班延误已经成为世界范围内一个日益严峻的问题,对于大型机场进行航班延误预警工作显得日趋紧迫。在空运需求与机场容量冲突导致航班延误的观点下,提出了用排列支持向量机来进行航班延误预警。针对机场航班数据不断更新的特点,给出了增量式排列支持向量机算法来进行航班延误预警。不仅通过人工数据上的实验说明了该增量式算法更能满足在线预警的要求,而且在收集到的中国某国际机场两周的航班数据上也取得了80%及以上的预测准确率。