列车轮轨黏着力在线估测计算方法

Online Estimation Algorithm of Adhesive Force for Train Wheeltrack

摘要:

针对列车的制动防滑控制，提出黏着力的在线估测计算方法.因为黏着力不易测量，如何实时监测黏着力大小以便充分利用轮轨黏着是防滑控制的关键.建立了轮对动力学模型，并采用卡尔曼滤波器、扩张状态观测器等，以轴速和车轮等效夹紧力作为可输入量，设计了5种黏着力在线估测计算方法.采用Simulink软件平台，设置了信号噪声污染和传输延迟，并仿真了黏着力不变和黏着力变化两种工况，结果显示5种算法都能对黏着力进行估测，但综合黏着力估测的响应时间和最大误差两个指标来看，非线性扩张状态观测算法对黏着力的估测效果是最好的.最后，采用实测数据，进一步验证了算法对黏着力估测的准确性.

Abstract:

Novel online estimation algorithms of train adhesive force were proposed for antiskid control. Since it is difficult to measure the adhesive force, it is of significance to make full use of adhesive force in antiskid control. In this paper, a wheel set dynamic model is established first. Then, using the Calman filter, extended state observer and so on, five online estimation algorithms for train adhesive force were designed, where the axle speed and the equivalent clamping force were the input. Furthermore, with the simulink software platform, signal noise contamination and transmission delay were set, and two conditions of constant adhesive force and variable adhesive force were simulated. Simulation results reveal that the five algorithms could be used to estimate the adhesive force, but when the response time and the maximum error of adhesive force estimation are taken into account, the nonlinear expansion state observation algorithm is the best algorithm for adhesive force estimation. Finally, the accuracy of the estimation algorithm is further validated by using measured data.