Locking control of underground signal lights based on improved Kalman filter and state observer
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摘要:
在非煤矿山井下斜坡道运输过程中，由于井下UWB动态定位精度不足、车辆定位卡采样间隔长和数据丢失等，传统信号灯闭锁控制方法效果较差。针对该问题，提出一种基于改进卡尔曼滤波和状态观测器的井下信号灯闭锁控制方法。分析了基于UWB的井下车辆定位原理，给出了适合非煤矿山井下特点的信号灯逻辑判定方法。提出一种强跟踪卡尔曼滤波算法，通过强跟踪自适应方式对卡尔曼滤波算法进行改进，在计算预测误差时加入时变渐消因子，提高定位精度；根据滤波后所得的后验距离与速度值预测出车辆到达门限的时间，解决离散数据采集导致的控制滞后性问题，提高信号灯闭锁的可靠性和及时性。采用远程状态观测器评估信号灯闭锁控制效果，基于时域自动跟踪的统计，实现了闭锁可靠性的量化评估。仿真结果表明，改进卡尔曼滤波算法后，车辆动态与静态位置误差分别降低25.67%和27.19%，动态与静态速度误差分别降低25.28%和34.73%，信号灯门限逻辑响应更快。井下工业性试验和应用结果表明，采用强跟踪尔曼滤算法后，井下信号闭锁成功率达99.5%以上，有效提高了井下斜坡道岔路口信号闭锁控制的实时性和可靠性，保障了井下车辆的安全行驶。
Abstract:During underground ramp transportation in non-coal mines, traditional signal light locking control method is ineffective due to insufficient UWB dynamic positioning accuracy, long sampling interval of vehicle positioning cards, and data loss. Aiming at this problem, this paper proposed a locking control method of underground signal lights based on improved Kalman filter and state observer. The underground vehicle positioning principle based on UWB was analyzed, and a signal light logic determination method tailored to the characteristics of non-coal mine operation was provided. A strong tracking Kalman filter algorithm was introduced, and it was improved through strong tracking adaptive method. A time-varying fading factor was incorporated in the calculation of prediction errors, improving the positioning accuracy. According to the filtered posterior distance and speed values, the time for the vehicle to reach the threshold was predicted, solving the problem of control lag caused by discrete data acquisition and improving the reliability and timeliness of signal light locking. A remote state observer was used to evaluate the performance of signal light locking control. Based on statistics of the time domain automatic tracking, the quantitative evaluation of the locking reliability was realized. Simulation results demonstrated that after improving Kalman filter algorithm, the dynamic and static position errors of the vehicle were reduced by 25.67% and 27.19%, respectively, the dynamic and static speed errors were reduced by 25.28% and 34.73%, respectively. The logic response of the signal light threshold was faster. Results from underground industrial trials and applications showed that the success rate of underground signal locking was over 99.5% after using the strong tracking Kalman filter algorithm, which effectively improved the real-time performance and reliability of signal locking control of underground ramp intersections and ensured the safe driving of underground vehicles.
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图 14 车辆测距结果滤波前后对比
Figure 14. Comparison of vehicle distance measurement results before and after filtering
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图 15 测距误差滤波前后对比
Figure 15. Comparison of distance measurement error before and after filtering
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图 19 滤波前后信号灯闭锁成功率对比
Figure 19. Comparison of the success rate of signal light locking before and after filtering
表 1 门限预测结果
Table 1 Threshold prediction results
组别 时刻 位置/cm 时间/ms 1 前一时刻 2 005 19 675 预测时刻 2 000 19 684 后一时刻 1 715 20 206 2 前一时刻 −1 883 34 872 预测时刻 −1 883 35 105 后一时刻 −2 173 35 401 3 前一时刻 −2 111 5 112 预测时刻 −2 000 5 357 后一时刻 −1 877 5 632 4 前一时刻 −1 970 14 918 预测时刻 −2 000 14 990 后一时刻 −2 389 15 947 
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表 2 信号灯闭锁结果
Table 2 Signal light locking results
序号 总次数 闭锁成功次数 未滤波 卡尔曼
滤波改进卡尔
曼滤波1 8 256 7 719 8 058 8 206 2 8 524 8 055 8 302 8 456 3 8 442 7 986 8 256 8 408 4 8 242 7 813 8 077 8 234 5 9 502 9 008 9 293 9 454 6 8 424 7 994 8 213 8 365 7 9 501 9 045 9 292 9 425 8 8 536 8 092 8 323 8 485 9 8 583 8 120 8 403 8 557 10 8 413 7 967 8 253 8 354 11 8 202 7 784 7 997 8 136 12 9 531 9 064 9 264 9 464 13 8 494 8 052 8 273 8 418 14 9 514 9 029 9 305 9 447 15 8 543 8 133 8 329 8 475 16 8 481 8 057 8 252 8 439 17 8 293 7 845 8 053 8 235 18 8 193 7 759 7 980 8 160 19 8 521 8 086 8 282 8 495 20 8 414 7 951 8 212 8 397 21 8 231 7 787 8 042 8 198 22 8 094 7 625 7 892 8 037 23 8 231 7 762 8 001 8 165 24 8 141 7 693 7 897 8 100 25 8 512 8 052 8 291 8 452 26 8 812 8 319 8 592 8 786 27 8 712 8 198 8 529 8 642 28 8 631 8 105 8 415 8 579 29 8 512 8 018 8 282 8 452 30 8 652 8 176 8 444 8 617
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