Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.     

三关节机器人的神经网络补偿自适应控制器设计

三关节机器人广泛用于工业生产、轮式或履带式排爆机器人,为了补偿由于机器人结构参数、作业环境干扰等不确定性因素造成的机器人动力学模型的不确定性,将机器人动力学模型分解为名义模型和误差模型两部分,其误差模型采用RBF神经网络进行补偿,得到其估计信息,神经网络的输出权值根据Lyapunov稳定性理论采用自适应算法进行调整。所设计的神经网络补偿自适应控制器解决了不确定性机器人动力学系统控制器设计的不确定性问题,同时,通过定义Lyapunov函数,证明了控制器能渐近、稳定地跟踪期望轨迹。机器人的3个关节在控制器的作用下,约在5 s时达到期望轨迹,神经网络约在5 s时逼近机器人动力学模型的误差模型,实验结果表明了机器人关节对期望轨迹具有良好的轨迹跟踪性能。     

GSVM:一种支持Gather/Scatter的向量存储器

宽单指令多数据流(Single Instruction Multiple Data, SIMD)架构数字信号处理器一般都能高效支持地址连续或等距跨步等规则应用的向量访存,但对于科学与工程计算中广泛存在的不规则应用的数据访存则带宽利用率往往较低,从而大幅降低了其整体运算能效。为了提高不规则应用的向量访存性能,基于某SIMD数字信号处理器的体系结构,设计了一种支持Gather/Scatter访存的向量存储器GSVM。通过设计与SIMD宽度相匹配的向量地址计算单元和合适深度的冲突缓冲器阵列,实现了Gather/Scatter指令向量地址计算、仲裁与缓存的全流水访存操作。实验结果表明,相比以前不支持Gather/Scatter访存的存储器,GSVM在增加22%的硬件代价基础上,基于稀疏矩阵向量乘的测试程序集获得了2～8的性能加速比。     

电磁轨道发射器动态发射过程的数值模拟

针对电磁轨道发射器动态发射过程的数值模拟问题,基于矢量磁位A和时间积分标量电位V,采用节点单元法,并选择运动坐标系描述运动问题,推导出动态发射下的电磁轨道发射器三维涡流场有限元离散方程。结合温度场控制方程,建立电磁轨道发射器的三维电磁-温度耦合有限元模型。针对一个电磁轨道发射器动态发射问题,对模型进行数值模拟,得到了动态发射下轨道及电枢的温度场分布特点及发射器电感梯度随时间的变化规律。计算结果表明,模型计算出的脉冲电流峰值、出口电流大小、出口速度等参数均与试验结果吻合较好,验证了所开发的有限元程序代码的正确性。     

虚拟环境下某型迫击炮自动机故障预测仿真分析研究

在对某迫击炮自动机的结构组成和机构动作分析的基础上,应用ADAMS软件建立了该武器自动机的虚拟样机模型,并结合实验数据和理论数据对自动机连发射击仿真结果的对比,验证了虚拟样机的正确性。以虚拟样机为工具,对自动机常见故障的影响因素进行了仿真分析和计算,为该武器的故障预测提供了理论依据和数据支持。     

基于动态逆的无人机机动控制律设计

针对现代无人机机动性的需求,基于动态逆系统理论,提出一种适合小型无人机机动指令跟踪的非线性控制方法,分别设计了慢变量和快变量动态逆控制律,并通过对无人机航迹倾斜角的实时导引控制,进一步提高无人机的整体机动性.仿真结果表明,基于动态逆的航迹倾斜角反馈控制能够达到期望的机动效果,具有良好的指令跟踪性能.该控制律可应用于无人侦察机规避高空障碍,具有重要的现实意义.     

双泵源电液舵机线谱噪声控制

采取变频调速控制策略的电液舵机可大幅降低操舵噪声，但系统线谱噪声明显且随工况变化而迁移，衰减难度大。基于此，结合泵源双冗余的电液舵机原理，仅改变原系统控制策略，提出基于辅助泵源合流的压力脉动控制措施。理论分析指出，两泵源转速一致，初始相位角为半个周期可衰减奇数次谐波。设计了主从同步控制策略，配置相应硬件，并应用于实际系统，实测相同操舵工况下可降低基脚结构振动总级3~5 dB，一阶线谱最大可衰减23 dB。