广西师范大学学报（自然科学版） ›› 2010, Vol. 28 ›› Issue (3): 175-181.

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基于神经网络的盾构机密封舱压力控制研究

曹丽娟1, 上官子昌2,3, 李守巨4, 毛忠义5   

  1. 1.大连海洋大学机械工程学院,辽宁大连116023;
    2.大连理工大学土木水利学院,辽宁大连116024;
    3.大连海洋大学土木工程学院,辽宁大连116023;
    4.大连理工大学工业装备结构分析国家重点实验室,辽宁大连116024;
    5.辽宁蒲石河抽水蓄能有限公司,辽宁丹东118000
  • 收稿日期:2009-12-05 出版日期:2010-09-20 发布日期:2023-02-06
  • 通讯作者: 曹丽娟(1959—),女,辽宁阜新人,大连海洋大学副教授。E-mail:clj@dlou.edu.cn
  • 基金资助:
    国家重点基础研究发展规划(973)资助项目(2007CB714006);国家自然科学基金重点项目(90815023)

Pressure Control for Headhamber of Shield Machine Based on Neural Network

CAO Li-juan1, SHANGGUAN Zi-chang2,3, LI Shou-ju4, MAO Zhong-yi5   

  1. 1. Marine Mechanical Engineering Institute,Dalian Ocean University,Dalian Liaoning 116023,China;
    2. School of Civil and Hydraulic Engineering,Dalian University of Technology,Dalian Liaoning 16023,China;
    3. Institute of Civil Engineering,Dalian Ocean University,Dalian Liaoning 116023,China;
    4. State Key Laboratory of Structure Analysis for Industrial Equipment,Dalian University of Technology, Dalian Liaoning 116024,China;
    5. Pushihe Pumped-Storage Co. LTD,Dandong Liaoning 118000,China
  • Received:2009-12-05 Online:2010-09-20 Published:2023-02-06

摘要： 根据从盾构机刀盘进入密封舱的渣土的体积与从螺旋输送机排除渣土的体积平衡原理以及渣土的非线性本构模型,建立了密封舱压力控制的离散状态方程。分别采用BP神经网络和PID神经网络作为辨识器和控制器,研究了盾构机密封舱压力控制时变系统的辨识和控制问题。数值仿真结果表明,所提出的基于神经网络的盾构机密封舱压力控制方法是有效的,并且具有良好的稳定性和鲁棒性。

关键词: 密封舱压力控制, PID神经网络, 辨识器, 控制器, 控制方程

Abstract: According to the volume balance principle that volumeof entering headchamber from cutterhead of shield machine is equal to the carried volume by screw conveyor,a discrete state equation is proposed for pressure control of headchamberin shield tunneling based on nonlinear constitutive model of waste soil.BP neural network and PID neural network are used as identifier and controller.The pressure control and model parameter identification of time-varying system of shield tunneling are studied.Numerical simulation result shows that the pressure control method based on neural network in shield tunneling is effective.The proposed algorithms have good stability and robustness.

Key words: pressure control of headchamber, PID neural network, identifier, controller, control equations

中图分类号: 

  • U455.43
[1] 潘登,郑应平.基于模型参考和内模原理的线性系统鲁棒控制设计[J].控制理论与应用,2007,24(4):51-56.
[2] 周德云,李兆强,曲艺海.离散模型参考变结构控制算法[J].系统仿真学报,2009,21(13):4053-4056.
[3] 刘成,赵福宇,侯素霞.一种新的多变量参考模型解耦控制的方法[J].控制工程,2009,16(1):12-16.
[4] YANG Hua-yong,SHI Hu,GONG Guo-fang,et al.Electro-hydraulic proportional control of thrust system for shield tunneling machine[J].Automation in Construction,2009,18(7):950-956.
[5] MORARI M,LEE J H.Model predictive control:past,present and future[J].Computers and Chemical Engineering,1999,23(4/5):667-682.
[6] LI Shou-ju,LIU Ying-xi.An improved approach to nonlinear dynamical system identification using PID neural networks[J].International Journal of NonlinearSciences and Numerical Simulation,2006,7(2):177-182.
[7] ZHU Xue-mei,HUA Chang-chun,WANG Shuo.State feedback controllerdesign of networked control systems with time delay in the plant[J].International Journal of Innovative Computing,Information and Control,2008,4(2):283-290.
[8] 周奇才,冯双昌,李君.盾构智能辅助分析系统中推力研究[J].科技导报,2006,28(21):57-60.
[9] 刘晓华,杨园华.基于观测器的不确定广义时滞系统鲁棒预测控制[J].控制与决策,2009,24(4):606-610.
[10] 戴文战,娄海川,杨爱萍.非线性系统神经网络预测控制研究进展[J].控制理论与应用,2009,26(5):521-530.
[11] PADHI R,PRABHAT P,BALAKRISHNAN S N.Reducedorder optimal control synthesis of a class of nonlinear distributed parameter systems using single network adaptive critic design[J].International Journal of Innovative Computing,Information and Control,2008,4(2):457-469.
[12] XU Hao-jian,IOANNOU P A.Robust adaptive control of linearizablenonlinear single input systems with guaranteed error bounds[J].Automatica,2004,40(11):1905-1911.
[13] KITTISUPAKORN P,THITIYASOOK P,HUSSAIN M A,et al.Neuralnetwork based model predictive control for a steel pickling process[J].Journal of Process Control,2009,19(4):579-590.
[14] AKESSON B M,TOIVONEN H T.A neural network model predictive controller[J].Journal of Process Control,2006,16(9):937-946.
[15] LAZAR M,PASTRAVANU O.A neural predictive controllerfor non-linear systems[J].Mathematics and Computers in Simulation,2002,60(3/5):315-324.
[16] MAYNE D Q,RAKOVIC S V,FINDEISEN R,et al.Robust output feedback model predictive control of constrained linear systems:time varying case[J].Automatica,2009,45(9):2082-2087.
[17] 兰海英,徐刚.非线性系统开闭环PID型迭代学习控制的收敛性分析[J].江西师范大学学报:自然科学版,2006,30(6):560-562.
[18] 黎克麟,曾意.具有多滞后的区间非线性Lurie控制系统的鲁棒绝对稳定性[J].四川师范大学学报:自然科学版,2007,30(1):27-30.
[19] 梁霄,刘瑶,姚云熙,等.开架式水下机器人运动的变结构神经网络控制[J].广西师范大学学报:自然科学版,2006,24(4):115-118.
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