基于FPGA和LMD18200的步进电机控制系统

提出了一种控制步进电机的设计方法,结合MCU、FPGA和增量型编码器构成了一个完整的运动控制平台。系统由总线接口单元、闭环控制单元、PWM脉宽调制单元和LMD18200驱动单元组成。实践表明,利用LMD18200驱动逻辑能够使步进电机高效稳定地运行,有效降低了电机运行中的噪声和起动、停止时的振动,使用效果好于大部分常规步进电机驱动方法。     

高速无人遥控潜水器永磁推进器的无位置传感器控制

高速电动无人遥控潜水器(ROV)用于浅海严酷水下环境中的观测和作业。ROV上的推进器电机内部充油,需用旋转变压器测量转子磁极位置,旋转变压器反馈的模拟信号容易受电机相线电流干扰。为此,提出了一种使用新型连续饱和函数的滑模观测器,通过使用连续饱和函数和锁相环方法,可以估算磁极位置。所提算法与传统滑模观测器相比,可有效减小电机运行时的抖振。通过仿真与试验,验证了该算法的可行性。     

采用定子电流与扰动观测的永磁同步电机改进预测电流控制

提出了一种采用定子电流和扰动观测补偿的改进预测电流控制(PCC)算法。理论上永磁同步电机PCC具有优异的控制性能,但现实系统中离散采样延时与电磁参数时变等问题使得原理上基于模型的预测电流控制器的控制品质严重恶化。设计了龙伯格(Luenberger)状态观测器实现对定子电流与参数扰动的观测,并应用于补偿和改进经典的无差拍预测电流控制器。将观测到的当前时刻的定子电流替代当前时刻的采样电流用于反馈控制,以补偿采样延时;运行过程中电磁参数的变化使得模型参数失配,其影响以电压扰动的形式被观测出来,并补偿到预测控制输出的电压指令中。仿真和试验结果验证了所提方法的有效性。     

A new weighted mean temperature model in China

The Global Positioning System (GPS) has been applied in meteorology to monitor the change of Precipitable Water Vapor (PWV) in atmosphere, transformed from Zenith Wet Delay (ZWD). A key factor in converting the ZWD into the PWV is the weighted mean temperature ($T_m$), which has a direct impact on the accuracy of the transformation. A number of Bevis-type models, like $T_m-T_s$ and $T_m-T_s\text{,}{P}_s$ type models, have been developed by statistics approaches, and are not able to clearly depict the relationship between $T_m$ and the surface temperature, $T_s$. A new model for $T_m$, called weighted mean temperature norm model (abbreviated as norm model), is derived as a function of $T_s$, the lapse rate of temperature, δ, the tropopause height, $h_{\mathit{trop}}$, and the radiosonde station height, $h_s$. It is found that $T_m$ is better related to $T_s$ through an intermediate temperature. The small effects of lapse rate can be ignored and the tropopause height be obtained from an empirical model. Then the norm model is reduced to a simplified form, which causes fewer loss of accuracy and needs two inputs, $T_s$ and $h_s$. In site-specific fittings, the norm model performs much better, with RMS values reduced averagely by 0.45 K and the Mean of Absolute Differences (MAD) values by 0.2 K. The norm model is also found more appropriate than the linear models to fit $T_m$ in a large area, not only with the RMS value reduced from 4.3 K to 3.80 K, correlation coefficient $R^2$ increased from 0.84 to 0.88, and MAD decreased from 3.24 K to 2.90 K, but also with the distribution of simplified model values to be more reasonable. The RMS and MAD values of the differences between reference and computed PWVs are reduced by on average 16.3% and 14.27%, respectively, when using the new norm models instead of the linear model.     

无人机微型舵机伺服控制器及其测试系统的设计

针对高性能的无人机微型舵机伺服控制系统,介绍了一种基于PIC单片机的高精度舵机伺服控制器及其测试系统.通过操纵测试软件,计算机发出模拟飞控计算机发送的位置信号,输入以单片机为主控制芯片的舵机控制器,控制功率驱动电路,驱动直流电机,实现伺服控制.控制器软件采用位置、速度和电流三闭环控制算法,利用模糊比例积分微分(PID)控制算法对位置环进行实时调整,同时在速度环与电流环引入积分分离PI控制算法.实验结果表明,控制器具有抗干扰性好、响应速度快、精度高和输出力矩大等特点.     

GNSS多径抑制基带处理算法综述CSCD

随着全球导航卫星系统(GNSS)信号体制的不断更新,接收机应用环境越来越复杂,多径误差抑制技术也在不断发展进步。其中,基于改进基带的多径抑制算法由于成本和效果综合较优,受到了国内外研究人员的重视。论文首先阐述了GNSS多径抑制的基本原理,综述了传统的GNSS参量式和非参量式多径抑制算法的研究现状和实现方式,然后探讨分析了新体制信号中提高抗多径性能的方法,总结了当前GNSS多径抑制基带处理研究中的主要问题,最后展望了其未来的发展趋势。     

旋转对固体火箭发动机两相流点火过程影响仿真研究

为研究高速旋转对内外燃管型装药固体火箭发动机凝聚相点火瞬态过程的影响规律，应用计算流体动力学（CFD）流体计算软件，使用用户定义函数（UDF）编程接口建立固体火箭发动机点火模型，对旋转条件下发动机凝聚相点火过程进行模拟。将数值计算结果与地面旋转实验内弹道进行对比分析，验证数值模型的正确性。计算结果表明：①点火药燃气颗粒因旋转做离心运动，大量粒子聚集在燃烧室头部上端，部分粒子附着在发动机壁面，且停留时间较长。②点火药燃气颗粒占比从20%增加到40%，点火压力峰值降低3.93%，发动机转速的升高会造成内弹道平衡压力升高，但点火压力峰会逐渐降低，且峰值出现时间发生延迟，转速达到15 000 r/min时点火压力峰消失。③转速增大，点火颗粒与推进剂传热增大，火焰传播期减小，但燃气填充期和点火延迟增大，点火药燃气颗粒占比为20%时，转速为15 000 r/min较静止条件下点火延迟增加了23.76%。     

Oblique shock train motion based on schlieren image processing

In this paper, shock train motion in a Mach number 2.7 duct is studied experimentally, and large numbers of schlieren images are obtained by a high-speed camera. An image processing method based on Maximum Correlation Detection (MCD) is proposed to detect shock train motion from the schlieren images, based on which the key structures, e.g., separation positions and separation shock angles on the top and bottom walls, can be analysed in detail. The oscillations of the shock train are generated by rhombus and ellipse shafts at various excitation frequencies. According to the analysis of MCD results, the distributions of the frequency components of shock train oscillation generated by the two shafts are distinctly different, in which the motion generated by the ellipse shaft is much smoother; shock train motion is mainly characterized by the oscillation of separation position while the separation shock strength is not so sensitive to downstream disturbance; there is a hysteresis loop relation between the downstream pressure and separation position.     

基于数字孪生的航班链延误动态预测模型

针对复杂多变的航班运行环境，提出一种基于数字孪生的航班链延误动态预测模型，以改善传统预测方法的精度及自适应性。模型基于数字孪生航班链系统构建，采用滑动窗口下的多通道特征建模完成单元级航班延误预测，并提出一种混合优化策略进行模型参数的动态优化，最后通过孪生数据驱动的链式分析方法实现了全航班链的延误分析与修正。采用国内航班数据进行实验，得到在各个窗口下的航班延误平均绝对误差（Mean absolute error, MAE）为11.79 min，低于其他基线模型和静态模型；且引入孪生数据驱动分析和修正后，紧随其后的航班预测误差比此前进一步降低了6.44%。结果表明，模型有利于数字孪生航班链系统实现虚实交互，并具有优良的预测精度和自适应性。     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.