A State-Space Approach to the Synthesis of Random Vertical and Crosslevel Rail Irregularities

区间(图论) 光谱密度 噪音(视频) 磁道(磁盘驱动器) 白噪声 状态空间 计算机科学 基质(化学分析) 功率(物理) 算法 数学 控制理论(社会学) 数学分析 统计物理学 物理 电信 统计 图像(数学) 组合数学 操作系统 人工智能 复合材料 量子力学 材料科学 控制(管理)
作者
R. H. Fries,B. M. Coffey
出处
期刊:Journal of Dynamic Systems Measurement and Control-transactions of The Asme [ASM International]
卷期号:112 (1): 83-87 被引量:40
标识
DOI:10.1115/1.2894143
摘要

Solution of rail vehicle dynamics models by means of numerical simulation has become more prevalent and more sophisticated in recent years. At the same time, analysts and designers are increasingly interested in the response of vehicles to random rail irregularities. The work described in this paper provides a convenient method to generate random vertical and crosslevel irregularities when their time histories are required as inputs to a numerical simulation. The solution begins with mathematical models of vertical and crosslevel power spectral densities (PSDs) representing PSDs of track classes 4, 5, and 6. The method implements state-space models of shape filters whose frequency response magnitude squared matches the desired PSDs. The shape filters give time histories possessing the proper spectral content when driven by white noise inputs. The state equations are solved directly under the assumption that the white noise inputs are constant between time steps. Thus, the state transition matrix and the forcing matrix are obtained in closed form. Some simulations require not only vertical and crosslevel alignments, but also the first and occasionally the second derivatives of these signals. To accommodate these requirements, the first and second derivatives of the signals are also generated. The responses of the random vertical and crosslevel generators depend upon vehicle speed, sample interval, and track class. They possess the desired PSDs over wide ranges of speed and sample interval. The paper includes a comparison between synthetic and measured spectral characteristics of class 4 track. The agreement is very good.
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