Improved model-free adaptive control for EMUs velocity tracking system
Article Text (Baidu Translation)
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摘要: 为了提高列控系统跟踪精度与平稳运行，提出了一种改进的多输入多输出(MIMO)无模型自适应控制(MFAC)方法；基于动态线性化技术，将系统各动力单元输入输出数据等效成更符合高速动车组实际运行特性的全格式动态线性化(FFDL)数据模型；通过在目标准则函数中加入输出误差率，并对输出误差和输出误差率进行加权融合，推导出新的带有输出误差率的无模型自适应控制(MFAC-OER)方案；通过对FFDL数据模型的外界扰动、参数误差等不确定项进行延时估计，进一步提升了算法的控制性能和对系统的等价描述程度；以实验室配备的CRH380A型动车组半实物试验平台对该方法进行仿真测试，使其跟踪济南—徐州的实际速度-位移曲线，并与传统算法进行对比。仿真结果表明：通过MFAC-OER方法得到的动车组各动力单元速度误差为[-0.151, 0.136] km·h-1，控制力和加速度分别在[-48, 42] kN和[-0.785, 0.687] m·s-2以内且变化平稳，控制性能优于比例积分微分方法和传统MFAC方法；整体仿真结果证明了MFAC-OER方法不仅能快速到达系统稳态并且具有良好的抗外界干扰特性，满足动车组跟踪精度与安全要求。Abstract: To improve the tracking accuracy and stable operation of the train control system, an improved multiple-input multiple-output (MIMO) model-free adaptive control (MFAC) method was proposed. Based on the dynamic linearization technology, the input-output data of each power unit of the system were equivalently transformed into a full form dynamic linearization (FFDL) data model that better fitted the actual operation characteristics of high-speed electric multiple units (EMUs). By incorporating the output error rates into the objective criterion function and weighting the fusion of output errors and output error rates, a new model-free adaptive control scheme with output error rates (MFAC-OER) was derived. The control performance of the algorithm and the equivalent description degree of the system were further improved by delayed estimation of uncertainty factors, such as external disturbances and parameter errors in the FFDL data model. The proposed method was simulated and tested on a CRH380A high-speed EMUs semi-physical test platform equipped in the laboratory to track the actual speed-displacement curve from Jinan to Xuzhou and compare it with some traditional algorithms. Simulation results show that the speed errors of each power unit of EMUs obtained by the MFAC-OER method are within [-0.151, 0.136] km·h-1, with the control force and acceleration smoothly varying in the ranges of [-48, 42] kN and [-0.785, 0.687] m·s-2, respectively. The proposed method outperforms the proportional-integral-derivative (PID) and traditional MFAC methods in the control performance. The overall simulation results show that the MFAC-OER method can not only quickly reach the steady state of the system but also possesses good resistance to external disturbances, meeting the tracking accuracy and safety requirements of the EMUs.
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图 6 CRH380A型动车组济南西至徐州东的实际速度曲线
Figure 6. Actual velocity curves of CRH380A EMUs from Jinan West to Xuzhou East
表 1 CRH380A型动车组模型参数
Table 1. Model parameters of CRH380A EMUs
参数 数值 动力单元1质量/kg 1.836×105 动力单元2质量/kg 1.123×105 动力单元3质量/kg 1.836×105 列车阻力系数ai/(N·kg-1) 5.2 列车阻力系数bi/[N·s2·(kg·m)-1] 3.6×10-2 列车阻力系数ci/[N·s2·(kg·m2)-1] 1.2×10-3 车钩弹性系数ki/(N·m-1) 2.0×107 车钩阻尼系数li/(N·s·m-1) 5.0×106 
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表 2 正常运行时各个控制方法的性能指标
Table 2. Performance indexes of each control method in normal operation
参数 MSE IAE MA MFAC-OER方法 0.048 317 0.785 MFAC方法 0.156 918 0.825 PID方法 0.374 1 812 0.906
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表 3 参数突变时各个控制方法的性能指标
Table 3. Performance indexes of each control method in case of parameter mutation
s 方法 PID方法 MFAC方法 MFAC-OER方法 上升时间 6 4 2 调节时间 55 27 13
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