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基于神经网络PID的疏浚管道泥浆流速控制

Slurry Flow Rate Control for Dredging Pipelines Based on Neural Network PID
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摘要 疏浚作业中,泥浆管道内物料的组成、粒径、浓度等随水下地形土质等变化很大,易造成流速波动甚至堵管、爆管等故障,因此泥浆流速稳定控制对泥浆输送的效率和安全具有重要意义;疏浚管道输送系统具有非线性、大时滞和参数时变等特征,传统PID控制方法效果不佳,故此将BP神经网络和传统PID控制算法相结合,并将其应用于泥浆流速控制中;以河海大学管道输送实验平台为对象,采用受控自回归CAR模型描述泥泵变频器频率与管道泥浆流速之间的关系,通过实验和数值处理对模型进行离线辨识;在此基础上通过仿真对比传统PID、单神经元PID和BP-PID的流速控制性能,发现BP-PID控制器的超调量仅为3.8%,响应时间为11 s,控制性能较好;最后通过在体积浓度-10%到-30%泥浆范围内,泥浆浓度小幅度和大幅度增减实验,对流速控制方法进行了验证,结果表明在浓度平缓或剧烈波动时,采用BP-PID控制算法的流速控制系统,均能够在保证输送安全的前提下,快速、稳定地达到目标流速,具有较好的自适应控制性能。 In dredging operations,the composition,particle size and concentration of the material in the mud pipeline vary greatly with the underwater topography and soil quality,which may cause faults such as the fluctuation of flow rate and even blockage and bursting of the pipeline.Therefore,the stable control of mud flow rate is of great significance to the efficiency and safety of mud conveying.The dredging pipeline conveying system has the characteristics of nonlinearity,large time delay and time-varying parameters,and traditional PID control methods are ineffective.Therefore,BP neural network and traditional PID control algorithms are combined and applied to the slurry flow rate control.Taking the pipeline conveying experimental platform of Hohai University as the object,a controlled autoregressive CAR model is used to describe the relationship between mud pump inverter frequency and pipe slurry flow rate,and the model is carried out the off-line identification through the experiments and numerical processing.On this basis,the flow rate control performances of conventional PID,single neural PID and BP-PID are compared by the simulation.It is found that the overshoot of the BP-PID controller is only reached by 3.8%and the response time by 11 s.Finally,the flow rate control method was validated by small and large variation of mud concentration with the volume concentration range of-10%to-30%.The results show that the flow rate control system with the BP-PID control algorithm can achieve the target flow rate quickly and stably with a good adaptive control performance,ensuring the safety of conveying when the concentration is calm or fluctuating drastically.
作者 蒋爽 刘世纪 高礼科 倪福生 JIANG Shuang;LIU Shiji;GAO Like;NI Fusheng(School of Mechatronics Engineering,Hohai University Changzhou 213022,China;Engineering Research Center of Dredging Technology of Ministry of Education,Hohai University Changzhou 213022,China)
出处 《计算机测量与控制》 2023年第11期198-203,220,共7页 Computer Measurement &Control
基金 国家重点研发计划专题项目(2018YFC040740405) 河海大学大学生创新训练项目(202210294109Z)。
关键词 疏浚工程 泥浆流速控制 泥泵管道输送实验台 受控自回归模型 神经网络PID 单神经元PID dredging engineering slurry flow rate control mud pump pipeline transportation bench controlled autoregressive model neural network PID single neural PID
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