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1.
GNSS/INS组合方式是下一代列控系统定位技术的发展趋势,但由于惯导系统累计误差较大,使得列车处于卫星信号失锁环境下定位性能降低。为解决这个问题,针对微机械惯性测量单元IMU确定性误差的3个主要误差项:非正交误差、零偏误差、刻度因数,建立加速度计和陀螺仪的误差模型,在此基础上详细推导标定原理并提出标定方案。将误差补偿结果应用于京沈高速铁路试验现场并由试验结果分析可知:该方法能有效提高IMU的测量精度,相较于补偿前测量误差降低80%以上;补偿之后的惯导系统在40s时间内的导航速度误差小于1m/s,位置误差在10m之内,满足定位需求,具有实际意义的工程应用价值。 相似文献
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针对扭矩传感器静态校准无法完全满足实际需要的难题,设计一种新型100 N·m负阶跃动态扭矩校准装置,并阐述该装置的工作原理和关键技术。校准装置由扭矩产生装置、连接系统、制动系统、信号处理控制系统和空气轴承系统组成,利用火工拔销器产生负阶跃动态扭矩,较大程度上降低负阶跃动态扭矩的下降时间,并采用空气轴承的支承方式提高动态扭矩的传递精度。通过对试验数据进行分析和处理,结果表明该装置负阶跃扭矩产生时间低于0.1 ms。不确定度分析结果显示,该装置的扩展不确定度U为4.22%,扩展因子k为2。 相似文献
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Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California. 相似文献
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This paper validates the prediction model embedded in a model predictive controller (MPC) of variable speed limits (VSLs). The MPC controller was designed based on an extended discrete first-order model with a triangular fundamental diagram. In our previous work, the extended discrete first-order model was designed to reproduce the capacity drop and the propagation of jam waves, and it was validated with reasonable accuracy without the presence of VSLs. As VSLs influence traffic dynamics, the dynamics including VSLs needs to be validated, before it can be applied as a prediction model in MPC. For conceptual illustrations, we use two synthetic examples to show how the model reproduces the key mechanisms of VSLs that are applied by existing VSL control approaches. Furthermore, the model is calibrated by use of real traffic data from Dutch freeway A12, where the field test of a speed limit control algorithm (SPECIALIST) was conducted. In the calibration, the original model is extended by using a quadrangular fundamental diagram which keeps the linear feature of the model and represents traffic states at the under-critical branch more accurately. The resulting model is validated using various traffic data sets. The accuracy of the model is compared with a second-order traffic flow model. The performance of two models is comparable: both models reproduce accurate results matching with real data. Flow errors of the calibration and validation are around 10%. The extended discrete first-order model-based MPC controller has been demonstrated to resolve freeway jam waves efficiently by synthetic cases. It has a higher computation speed comparing to the second-order model-based MPC. 相似文献
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