A high-safety active/passive hybrid control approach for compressor surge based on nonlinear model predictive control

Surge active control can expand the stable operating range of the compressor. However, the difficulty of flow measurement, dynamic uncertainty disturbance, actuator delay characteristics, hard constraints of control variable, and system security measures have not been fully considered in the existing active control system, which significantly hinders its engineering application. Therefore, a nonlinear model predictive surge active control method is first presented based on flow estimator designed by using a continuous-time Kalman filter for dealing with the hard constraint of control variable and the impact of actuator delay of compression system with dynamic uncertainty. Then, a high-safety active/surge passive hybrid control strategy is designed, dominated by the surge active control and supplemented by the surge passive control, to ensure the compression system’s safe and stable operation. Lastly, the simulation results suggest that the flow estimator accurately estimates the compressor flow. When considering the delay impact of the actuators and sensors and measurement noise on the system, the proposed method exhibits stronger robustness than the existing methods. The active/surge passive hybrid control strategy can successfully ensure the compression system's safe and stable operation. This paper is of high practical significance for the engineering application of future compressor surge active control technologies.     

基于模型定义的复合材料旁路挤压试验压缩夹具参数化设计

针对民用飞机复合材料许用值试验开展中需要设计大量构型类似而繁杂的试验夹具,为提高夹具设计的效率和准确性、促进设计的标准化,本文总结了 ASTM标准中复合材料旁路挤压试验的压缩试验夹具构型设计和制造等相关信息,采用包含基于模型的定义(Model Based Definition,MBD)信息的CATIA二次开发方法,提出...     

固定翼无人机编队的启发分布式模型预测控制

针对无人机编队飞行控制中队形保持、路径规划、重构、防撞等多重功能需求,采用启发型分布式模型预测控制框架,将需求转化为多目标优化问题中的代价函数与相容性约束求解,实现了对无人机编队飞行的一体化控制.同时,采用分阶段式的轨迹规划算法,融合模型预测轨迹规划与多项式轨迹规划,利用多项式轨迹系数实现高效的邻居轨迹信息交互,从而增大了模型预测控制的预测范围,并降低了规划控制过程中的计算量与通信量.最后,通过9架无人机的协同避障编队仿真,证明了该方法的有效性.     

Investigation of the planar midlatitude ionospheric trend under different levels of solar activity

A better understanding of the ionosphere through accurate mathematical models is no doubt a crucial element. This study focuses on the challenging problem of building a model representing the complex structure of the midlatitude ionosphere. Previous studies have shown that a regional planar model is suitable in representing the total electron content (TEC) trend in the midlatitude ionosphere in both hemispheres. In this study, the planar trend model for 12 non-overlapping northern hemisphere regions in three groups of geographically near 4 regions is further investigated under different levels of solar activity; low, moderate and high. To that end, the coefficients of the model are estimated in the least squares sense using total electron content values from global ionospheric maps (GIMs) for the years 2009, 2012 and 2014. Subsequently, these coefficients are used to reconstruct estimated TEC maps which are then compared with actual GIM-TEC by investigating their difference in normalized $L_2$ norm squared sense. The regional planar trend model provides a particularly successful representation in the years 2012 and 2014 for which the solar activity level is the dominant factor determining the TEC trend. Under low solar activity conditions of 2009, other factors such as ocean currents, temperature variations and meteorological phenomena are suspected to have a considerable effect in some regions depending on their geographic location and on seasonal trends in those regions. As an example, studies show that under the influence of the Pacific Decadal Oscillation (PDO) and Siberian High (SH), a significant cooling trend between 2004 and 2018 in autumn is observed in Eurasia, which, in conjunction with the low solar activity levels, may be related to the deviations from the actual GIM-TEC in 2009 in these regions. As solar radiation increases, however, such bottom-side forcings are masked in 2012 and 2014 and these deviations are no longer observed.