【摘要】 由于地铁受电弓—接触线系统受电力列车运行速度限制，采用高空刚性架设接触供电方式.接触网实时参数测量和整定过程中，主要取决精密仪器结构状态与测量参数误差校正.传统的测量仪器采用激光相机与传感器组合方式，其数据传输整定慢、计算复杂融合难度大、受地铁隧道环境影响模型辨识度低等缺点.文中提出一种基于模糊理论的MPC(Model Prediction Control，模型预测控制)算法，在实时测量目标点定位和数据优化精度方面，与PID控制策略比较. AME/simulink试验仿真表明，基于模糊理论的MPC预测算法能提高测量仪器定位点位置预测校正，测量点轨迹协同精度提高15%，减少软件计算数据的作业量20%，提高数据处理精准度±10 mm.更多还原

【Abstract】 Due to the speed limitations of electric trains on the metro pantograph-catenary system, an overhead rigid contact power supply method is adopted. During the real-time parameter measurement and adjustment of the catenary, the process primarily depends on the structural condition of precision instruments and the error correction of measurement parameters. Traditional measurement instruments employ a combination of laser cameras and sensors, which have drawbacks such as slow data transmission and calibration, complex calculations, high difficulty in fusion, and low model recognition accuracy due to the influence of subway tunnel environments. The article proposes an MPC(Model Prediction Control) algorithm based on fuzzy theory, and compares it with the PID control strategy in terms of real-time measurement of target point positioning and data optimization accuracy. AME/Simulink experimental simulation shows that the MPC prediction algorithm based on fuzzy theory can improve the position prediction and correction of measuring instrument positioning points, increase the collaborative accuracy of measuring point trajectories by 15%, reduce the workload of software calculation data by 20%, and improve the data processing accuracy by ±10 mm.更多还原