为解决盾构在复杂地层施工时推进速度和压力难以控制的问题，在压力流量控制的基础上提出BP神经网络控制策略。通过AMESim建立推进系统物理模型，并利用Simulink设计出BP神经网络控制器，最后对系统进行联合仿真，分析推进系统液压缸在变流量和变负载工况下推进速度和压力的响应特性。仿真结果表明： 该控制策略与常规PID控制相比，波动幅度降低，调节时间快。采用BP神经网络PID控制能够有效地提高盾构在负载突变情况下速度和压力控制精度，稳定性好、适应能力强，为盾构控制系统设计和优化提供理论参考。

The BP neural network control system is proposed on the basis of pressure flow control so as to control the speed and pressure during shield tunneling in complex strata. The physical model of shield thrusting system is established by AMESim; and then the BP neural network controller is designed by Simulink; finally, the system is simulated so as to analyze the response characteristics of thrusting speed and velocity of hydraulic thrusting control cylinder of shield machine under variable flow and variable load conditions. The simulation results show that: 1) Compared to conventional PID controller, the fluctuation amplitude of BPPID controller is lower and the adjusting response time is shorter. 2) The control accuracy of thrusting speed and pressure of shield machine under loading condition can be improved by using BP neural network PID controller. 3) The practice shows that the abovementioned controller has good stability and adaptability, so as to provide theoretical reference for design and optimization of shield control system.