基于BPPID的疏浚管道稳定流速控制

BPPID-based Steady Flow Control for Dredged Pipelines

针对疏浚管道输送过程中,泥浆管道流速难以控制,输送泥浆所需功耗较大、管道磨损严重甚至会出现堵管、爆管等风险,文章通过河海大学自主研发的疏浚泥泵管道输送实验台和MATLAB仿真手段,对绞吸挖泥船管道输送的稳定控制方法进行研究;在采用系统辨识对实验台进行建模的基础上,提出一种基于BP神经网络的PID控制器,将BPPID与传统PID控制器进行仿真对比分析,并利用模型实验台分别进行了流速阶跃实验和流速跟踪实验;仿真和实验结果表明,BPPID控制器具有自适应自学习能力,在工况复杂多变的环境中,随时间推移具有更好的系统响应速率,并能大幅度降低控制系统的超调量,适用于对超调量较为敏感的泥浆管道输送系统,为实际绞吸挖泥船输泥管道的稳定流速控制提供参考。

Aiming at the dredging pipeline conveying process,it is difficult to control the mud pipeline flow rate,the required power consumption for the conveying mud is large,the pipeline wear is serious and even occurs the risk of plugging and bursting,etc.In this paper,A dredging mud pump pipeline conveying experimental bench which developed independently by Hohai University,and the method of MATLAB simulation is used to study the stability control method of cutter dredger pipeline conveying.On the basic modeling of experimental bench using the system identification,a PID controller based on the BP neural network is proposed,the BPPID controller is simulated and compared with the traditional PID controller,respectively carry out the flow rate step and tracking experiment by using the model experimental bench.The simulation and experimental results show that the BPPID controller has adaptive self-learning capability.Under the complex and changing environment,the BPPID controller has better the response rate and over time of the system and can significantly reduce the overshoot of the system with time.In these cases,the BPPID controller is suitable for the mud pipeline conveying system which is sensitive to the overshoot.By the simulation and experiment,a reference for the stable flow rate control is provided by the actual winch dredger mud conveying pipeline.