基于BP改进PID的掘进机闭环回路跟随误差联合仿真

Co-simulation of closed-loop tracking error of shield machine based on BP improved PID

掘进机被广泛应用于煤矿巷道施工等领域,传统利用PID算法控制方式对推进参数进行了偏差修正,极大降低了协调性,在推进期间将会产生冲击甚至明显的振动现象。本方案从推进系统的工作原理层面出发,为系统构建了AMEsim模型,通过仿真测试获得系统速度与压力参数;通过Simulink算法构建BP神经网络控制器,以BP神经网络与PID方法进行联合控制,由此计算获得系统的压力与速度变化曲线。选择AMEsim和Simulink联合仿真,油缸快速达到80 mm/min稳定值,速度和压力没有明显波动。相对于PID控制方法,以BP改进PID进行融合控制时,能够对压力与流量不恒定的系统获得更优控制效果,具备更强的适应性。

Roadheader is widely used in coal mine roadway construction and other fields.The traditional PID algorithm control method is used to correct the deviation of propulsion parameters,which greatly reduces the coordination and will produce impact or even obvious vibration phenomenon during the propulsion.The scheme is triggered from the working principle of the propulsion system,and the AMEsim model is built for the system,and the system speed and pressure parameters are obtained through the simulation test.The BP neural network controller was built by Simulink algorithm,and the BP neural network and PID method were used for joint control.The pressure and velocity curves of the system were calculated.By co-simulation of AMEsim and Simulink,the cylinder quickly reaches the stable value of 80 mm/min,and the speed and pressure do not fluctuate significantly.Compared with PID control method,BP improved PID fusion control can achieve better control effect for the system with unsteady pressure and flow rate,and has stronger adaptability.