基于RBF神经网络的隧道掘进机推进自适应PID控制

摘要/Abstract

摘要： 针对全断面隧道掘进机的推进压力和推进速度的匹配问题，应用RBF神经网络算法设计了使推进压力和推进速度匹配且推进速度能快速跟随设定目标的自适应控制器。先在MATLAB中建立推进控制系统仿真模型，分析控制器自适应控制效果，然后在AMESim中建立推进系统液压控制模型，并与MATLAB联合仿真。联合仿真验证该控制器能在刀盘负载压力波动突变的情况下，使刀盘推进速度和推进压力跟随设定目标。试验证明，该控制器对负载大范围扰动有很好的抑制能力，能明显提高推进速度和推进压力耦合度并减小两者的波动范围。

关键词: 全断面隧道掘进机, 液压控制, 自适应控制, 大扰动

Abstract: The matching problems for full cross section TBM thrust pressures and thrust speeds, the applications of RBF neural network algorithm were designed to make the thrust pressures and the thrust speeds matching and speed adaptive controller might quickly follow the set goals. The controller was simulated by MATLAB, and the hydraulic control model of propulsion systems was established in AMESim and co-simulation with MATLAB. By joint simulation, the controller might make the cutter driving speeds and thrust pressures follow the set target when the cutter load pressures were abrupt. Experiments show that the controller has a good ability to suppress the load disturbances, and may improve the propulsion speeds obviously and push the pressure coupling degrees and reduce the fluctuation ranges of the both.

Key words: full cross section tunnel boring machine(TBM), hydraulic control, adaptive control, large disturbance

中图分类号:

TH137

宋立业;万应才. 基于RBF神经网络的隧道掘进机推进自适应PID控制[J]. 中国机械工程.

SONG Liye;WAN Yingcai. Adaptive PID Control Based on RBF Neural Network for TBMs[J]. China Mechanical Engineering.

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