EMD滤波方法在NMR测井中的应用

核磁共振测井中,回波信号的信噪比对T2谱反演及后续地层解释评价过程有重要影响。基于核磁共振测井信号与噪声的分析,提出了一种采用自适应性较强的经验模态分解（ EMD）算法对核磁共振测井原始数据作消噪处理。结果表明,采用该滤波算法,实验室回波数据信噪比由22 dB提高到46 dB,标准油井回波数据信噪比由11 dB提高到31 dB,反演得到的T2分布更加精确,多次重复测量中孔隙度结果有较高的一致性。     

基于RBF神经网络PID控制的列车ATO系统优化

针对在复杂环境下列车高速运行时,现有的Fuzzy-PID控制算法自适应性差在受到外界因素的干扰时会导致列车追踪误差较大的问题,提出了一种基于径向基(RBF)神经网络PID控制的列车速度控制算法。首先,在构建列车优化模型时,充分考虑列车经过电分相时必须处于惰行工况的特点,并且依据电分相和限速条件的特点将列车行驶过程中的区段进行了划分,简化了求解过程;然后使用RBF神经网络PID控制器对目标速度曲线进行追踪仿真,同时与现有的Fuzzy-PID控制器进行比较。实验结果表明,基于RBF神经网络PID控制算法能够实时有效的追踪目标速度曲线且追踪误差较小。     

火车车轮辐板外侧面裂纹成因分析

针对火车车轮在热成型过程中车轮辐板外侧面易产生裂纹问题,采用低倍检验、金相组织和化学成分分析等方法对裂纹产生原因进行了探讨。结果表明,裂纹的产生是由于车轮辐板在热成型中受到强烈的冷却影响所致,从而采取了相应的改进措施,取得了较好效果。     

火车车轮踏面快速检测系统的研究

针对火车车轮踏面的形状特点，研究了车轮踏面快速检测的方法，进而对检测数据进行了建模、优化，给出了数控加工的最佳切削量。在此基础上，进一步分析了如何在数控系统中快速构建车轮踏面检测系统，并在华中数控系统中得到实际应用。     

接收方预约信道的水声传感器网络MAC协议

为了获得更高的网络吞吐量,更低的端到端时延,设计出了一种适用于分簇网络的水声传感器网络MAC协议,该协议采用接收方建立握手机制,有效的获取邻居节点通信时的状态,动态分配发送时隙,实现数据包以报文序列方式无冲突的到达接收端.仿真结果表明,与同类MAC协议(FERI, RIPT)相比,该协议提高了网络吞吐量,降低了数据丢失率和端到端时延.     

基于Abaqus的高速列车车体传热系数仿真优化计算

以静止状态下某高速列车车体为研究对象,将其复杂的内外热交换系统简化为车体壁和冷桥两部分,运用Abaqus仿真计算得到整车传热系数;与相似设计的动车组车体实验结果对比可知仿真结果合理可靠.编写Python脚本实现在Abaqus后处理中显示车体壁传热系数K值云图的功能,以便于指导车体结构的优化设计;根据车体的原始设计并将选材成本考虑在内,提出的三种车体结构优化方案,使得整车传热系数降低10%.     

北京轨道交通燕房线（主线）工程阎村北停车场停车列检库工艺设计

北京市轨道交通燕房线为全自动无人驾驶线路,停车场内停车列检库也纳入了无人驾驶线路中进行管理。这对停车列检库的设置提出了一些新的要求。从停车列检库的设置规模、形式和全自动无人驾驶的特点进行了阐述,并提出了停车列检库的工艺设计,以满足平日车辆停放、列检和全自动无人驾驶的需求。     

Automatic Train Operation Based on Adaptive Terminal Sliding Mode Control

This paper presents an adaptive terminal sliding mode control(ATSMC) method for automatic train operation. The criterion for the design is keeping high-precision tracking with relatively less adjustment of the control input. The ATSMC structure is designed by considering the nonlinear characteristics of the dynamic model and the parametric uncertainties of the train operation in real time. A nonsingular terminal sliding mode control is employed to make the system quickly reach a stable state within a finite time, which makes the control input less adjust to guarantee the riding comfort. An adaptive mechanism is used to estimate controller parameters to get rid of the need of the prior knowledge about the bounds of system uncertainty. Simulations are presented to demonstrate the effectiveness of the proposed controller, which has robust performance to deal with the external disturbance and system parametric uncertainties. Thereby, the system guarantees the train operation to be accurate and comfortable.     

Optimal Terminal Iterative Learning Control for the Automatic Train Stop System

The train stop control is a typical set‐point control task, where only the final state (i.e., the terminal train stop position) is of concern and specified. For such a control problem, an optimal terminal iterative learning control (TILC) approach is presented in this paper, where the stopping position and initial braking speed are chosen as the terminal system output and the control input, respectively. The controller design only depends on the measured input/output (I/O) data without requiring any modeling information of the train operation system, and the learning gain is updated by the system I/O data iteratively to accommodate the system uncertainties. The monotonic convergence of the terminal tracking error is guaranteed by rigorous mathematical analysis. Extensive simulation results are provided to show the applicability and effectiveness of the proposed approach.     

Immersion and Invariance Based Robust Adaptive Control of High‐Speed Train with Guaranteed Prescribed Performance Bounds

This paper proposes a novel robust adaptive algorithm for train tracking control with guaranteed prescribed transient and steady‐state performance. As speed increases, the inherent time‐varying uncertainties and unmodeled dynamics in the longitudinal dynamics of a high‐speed train seriously impacts the tracking performance of automatic train operation. To improve train operation performance, an estimator based on immersion and invariance technology is developed to recover the unknown and time‐varying plant parameters, and it renders the estimation error converging to a bounded residual set exponentially while providing more freedom for the controller. After certain error transformation, the prescribed tracking performance is introduced into the controller design. Then, an input‐to‐stable stable controller is developed through the backstepping technique, and it is proven that stabilization of the transformed system is sufficient to guarantee the prescribed performance. Rigorous theoretical analysis for the presented algorithm is provided, and a series of simulation studies also are given to verify the effectiveness of it.