灌浆压力控制系统的关键技术研究
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摘要
灌浆是国内外大坝基础施工中常用的加固手段,灌浆压力是影响灌浆质量的关键参数之一。由于灌浆过程中存在非线性、地层变化的不确定性及参数耦合等特性,现行的手动控制方式控制精度差,工程的安全性、经济性无法得到保证。因此,开展灌浆压力自动控制的关键技术研究具有很高的理论价值和应用意义。
以贵州省科技计划重点项目“LJ系列灌浆压水测控系统在乌江水电开发中的开发及应用(2005J-069)”为依托,围绕灌浆系统中压力参考曲线的设计、主参量压力的软测量、灌浆压力的优化控制及灌浆控制系统研制开展研究,实现了灌浆压力的自动控制。主要研究内容及创新点如下：
1.提出了混合支持向量T-S(Takagi-Sugeno)模型的灌浆地层智能识别方法。
在支持向量机统计学习理论、核函数、嵌入式优化核参数及T-S模糊模型的研究基础上,分析四大类地层的压水试验数据分布特征,提出了基于压水试验数据和混合支持向量T-S模型的地层识别新方法,结合地层识别类别和灌浆规范设计压力曲线。该算法中利用优化RBF核的支持向量机训练已知地层样本获取支持向量,基于支持向量构造新型支持向量模糊T-S规则模型,并引入t-uninorm算子来提高识别准确率。对比仿真表明,该方法识别准确率高,运算时间短,且决策安全性好。实践证明该方法比常规地层检测方法更能应对地层的不确定性变化。
2.建立了灌浆压力的自适应T-S(Takagi-Sugeno)模糊软测量模型。
提出了灌浆压力的新型T-S模糊软测量方法,解决了深孔中灌浆压力直接测量难的问题。以返浆管道流体为对象,利用有限元软件分析不同参数对管道摩擦损失压力的影响,并结合灌浆压力简化数学模型,为软测量模型选择合适的辅助变量。模糊T-S模型的规则根据实际输入输出数据和辩识精度要求自适应产生,模糊模型结论参数的辩识采用卡尔曼递推算法,避免了后件参数辨识过程矩阵求逆的复杂性。与模糊聚类算法和误差反馈学习算法对比仿真,该模糊模型兼有建模精度高、运算速度快、自适应能力强,解决了在工况变化、地层变化下灌浆压力及时、准确测量。已在实际工程验证该模型的有效性,可以为控制系统提供反馈主参量。
3.提出了基于多模型自适应微粒群优化的灌浆压力控制新策略。
在软测量估计主反馈灌浆压力参数的基础上,提出了基于多模型的控制器参数优化新策略。该方法中将串级控制、前馈控制与多模型控制器参数优化方法整合后,解决了控制系统压力波动大和地层变化时控制器参数整定难的问题。该控制系统外环采用模糊控制器稳定灌浆压力,增加内环流量的PID控制,抑制了灌浆密度的干扰。而灌浆密度根据规范采取模糊前馈控制技术,达到既进行压力控制又能节约工程成本的目的。控制器参数根据不同地层吸浆流量基准模型采用自适应微粒群算法离线计算。仿真结果表明,该控制策略下压力跟踪效果好,抗干扰能力强,满足了系统在地层环境变化时的控制要求。
4.研制了灌浆压力硬件控制系统和设计了相应的软件平台,首次实现了灌浆压力的多模型优化控制。
研究灌浆控制系统中压力、流量和密度的高精度大循环灌浆检测方式并研制了带高压胶套隔离器的压力传感器和非接触密度检测计,为灌浆压力的自动控制提供了可靠的数据来源。在此基础上,结合灌浆规范和前一章控制结构与优化算法,开发了灌浆压力控制系统试验台及配套灌浆压力软件系统,首次实现了灌浆压力的串级优化控制。试验表明串级自动控制系统在控制精度、稳定性及抗干扰能力上优于人工控制系统。
Pressure grouting is an ordinary process in which pressure is an important factor to the quality of dam. Traditionally, the low-precision manual control style can not guarantee the security, economical efficiency of grouting project. Aiming at the nonlinear, uncertainty, strong coupling and time-varied of grouting process, research on key technologies of pressure control system has high theoretical value and applied significance.
Supported by the key scientific project of Guizhou(2005J-069), the author carried out the study in four areas:the design of pressure reference curve, soft sensing of grouting pressure, optimal control and developing the control system and realized the automatic operation for grouting system. The main content of study and innovation are ordered as follows:
1. An new intelligent identification method was developed based on hybird support vector Takagi-Sugeno)(SVMTS) for grouting stratum.
Based on the study of SVM theory, SVM kenerl function, SVM parameters optimization algorithm, T-S model and analysis the dis-tributing rules of water pressure test statistical data for different stratum, a new method of fuzzy-rule decision through SVM learning was proposed using pressurized water experimental data. Firstly, fuzzy rules through optimizational RBF SVM training model was obtained, next the "black-box" SVM decision function was expressed as the T-S fuzzy rules model and t-uninorm was adopted. Simulation results show that the new method improves identification accuracy, computing speed and decision process security, which is superior to RBF-NN method. The method has been applied to real project, which demonstrates the method can provide more scientific grouting pressure curve for control system.
2. Grouting pressure soft-sensor model was established based on an adaptive T-S fuzzy method.
T-S fuzzy soft-sensor model of pressure grouting was established to resolve the deep hole pressure measurement. The major auxiliary were selected using the finite element analysis software to study fluid dynamics pressure friction loss factors, combined with simplified mathematical model. The numbers of fuzzy rules are matched to the actual identification accuracy, and the parameters identification of fuzzy model are adopted as Kalman recursive algorithm. Simulation results show that the adaptive fuzzy algorithm is superior to fuzzy clustering algorithm and the error feedback learning in the modeling accuracy and computational speed. The soft sensing model has been by real project, which can provide the effective feedback pressure parameter.
3. A new control strategy was proposed based on adaptive particle swarm optimization(APSO) for multi-models of the grouting pressure control system.
On the basis of pressure soft sensing, a hybrid control strategy was proposed, which the controller parameters were optimized by APSO on multi-model of control system. Cascade control, feedforward contol and optimization were integrated in the new control method to avoid pressure fluctuation and parameter re-tuning. The outer loop was adopted fuzzy controller, inner loop adopted PID controller, grouting density is tuned by fuzzy feedforward method on the purpose of saving the project cost. controller parameters are optimized with APSO. Simulation results show that the control strategy obtain good tracking control effect in different stratum environment.
4.Hardware system and software system of control system were firstly developed to realize the pressure automatic control.
Research on the high-precision measurement ways of grouting pressure, flow and density parameters of control system, and design pressure sensor with high-pressure plastic isolator and non-contact density detector, which provide accurate data for control system. The algorithm above was firstly used to the grouting control experimental system, the results demonstrate the control strategy is superior to manual control ways in control accuray, stability and antijamming capability.
以贵州省科技计划重点项目“LJ系列灌浆压水测控系统在乌江水电开发中的开发及应用(2005J-069)”为依托,围绕灌浆系统中压力参考曲线的设计、主参量压力的软测量、灌浆压力的优化控制及灌浆控制系统研制开展研究,实现了灌浆压力的自动控制。主要研究内容及创新点如下：
1.提出了混合支持向量T-S(Takagi-Sugeno)模型的灌浆地层智能识别方法。
在支持向量机统计学习理论、核函数、嵌入式优化核参数及T-S模糊模型的研究基础上,分析四大类地层的压水试验数据分布特征,提出了基于压水试验数据和混合支持向量T-S模型的地层识别新方法,结合地层识别类别和灌浆规范设计压力曲线。该算法中利用优化RBF核的支持向量机训练已知地层样本获取支持向量,基于支持向量构造新型支持向量模糊T-S规则模型,并引入t-uninorm算子来提高识别准确率。对比仿真表明,该方法识别准确率高,运算时间短,且决策安全性好。实践证明该方法比常规地层检测方法更能应对地层的不确定性变化。
2.建立了灌浆压力的自适应T-S(Takagi-Sugeno)模糊软测量模型。
提出了灌浆压力的新型T-S模糊软测量方法,解决了深孔中灌浆压力直接测量难的问题。以返浆管道流体为对象,利用有限元软件分析不同参数对管道摩擦损失压力的影响,并结合灌浆压力简化数学模型,为软测量模型选择合适的辅助变量。模糊T-S模型的规则根据实际输入输出数据和辩识精度要求自适应产生,模糊模型结论参数的辩识采用卡尔曼递推算法,避免了后件参数辨识过程矩阵求逆的复杂性。与模糊聚类算法和误差反馈学习算法对比仿真,该模糊模型兼有建模精度高、运算速度快、自适应能力强,解决了在工况变化、地层变化下灌浆压力及时、准确测量。已在实际工程验证该模型的有效性,可以为控制系统提供反馈主参量。
3.提出了基于多模型自适应微粒群优化的灌浆压力控制新策略。
在软测量估计主反馈灌浆压力参数的基础上,提出了基于多模型的控制器参数优化新策略。该方法中将串级控制、前馈控制与多模型控制器参数优化方法整合后,解决了控制系统压力波动大和地层变化时控制器参数整定难的问题。该控制系统外环采用模糊控制器稳定灌浆压力,增加内环流量的PID控制,抑制了灌浆密度的干扰。而灌浆密度根据规范采取模糊前馈控制技术,达到既进行压力控制又能节约工程成本的目的。控制器参数根据不同地层吸浆流量基准模型采用自适应微粒群算法离线计算。仿真结果表明,该控制策略下压力跟踪效果好,抗干扰能力强,满足了系统在地层环境变化时的控制要求。
4.研制了灌浆压力硬件控制系统和设计了相应的软件平台,首次实现了灌浆压力的多模型优化控制。
研究灌浆控制系统中压力、流量和密度的高精度大循环灌浆检测方式并研制了带高压胶套隔离器的压力传感器和非接触密度检测计,为灌浆压力的自动控制提供了可靠的数据来源。在此基础上,结合灌浆规范和前一章控制结构与优化算法,开发了灌浆压力控制系统试验台及配套灌浆压力软件系统,首次实现了灌浆压力的串级优化控制。试验表明串级自动控制系统在控制精度、稳定性及抗干扰能力上优于人工控制系统。
Pressure grouting is an ordinary process in which pressure is an important factor to the quality of dam. Traditionally, the low-precision manual control style can not guarantee the security, economical efficiency of grouting project. Aiming at the nonlinear, uncertainty, strong coupling and time-varied of grouting process, research on key technologies of pressure control system has high theoretical value and applied significance.
Supported by the key scientific project of Guizhou(2005J-069), the author carried out the study in four areas:the design of pressure reference curve, soft sensing of grouting pressure, optimal control and developing the control system and realized the automatic operation for grouting system. The main content of study and innovation are ordered as follows:
1. An new intelligent identification method was developed based on hybird support vector Takagi-Sugeno)(SVMTS) for grouting stratum.
Based on the study of SVM theory, SVM kenerl function, SVM parameters optimization algorithm, T-S model and analysis the dis-tributing rules of water pressure test statistical data for different stratum, a new method of fuzzy-rule decision through SVM learning was proposed using pressurized water experimental data. Firstly, fuzzy rules through optimizational RBF SVM training model was obtained, next the "black-box" SVM decision function was expressed as the T-S fuzzy rules model and t-uninorm was adopted. Simulation results show that the new method improves identification accuracy, computing speed and decision process security, which is superior to RBF-NN method. The method has been applied to real project, which demonstrates the method can provide more scientific grouting pressure curve for control system.
2. Grouting pressure soft-sensor model was established based on an adaptive T-S fuzzy method.
T-S fuzzy soft-sensor model of pressure grouting was established to resolve the deep hole pressure measurement. The major auxiliary were selected using the finite element analysis software to study fluid dynamics pressure friction loss factors, combined with simplified mathematical model. The numbers of fuzzy rules are matched to the actual identification accuracy, and the parameters identification of fuzzy model are adopted as Kalman recursive algorithm. Simulation results show that the adaptive fuzzy algorithm is superior to fuzzy clustering algorithm and the error feedback learning in the modeling accuracy and computational speed. The soft sensing model has been by real project, which can provide the effective feedback pressure parameter.
3. A new control strategy was proposed based on adaptive particle swarm optimization(APSO) for multi-models of the grouting pressure control system.
On the basis of pressure soft sensing, a hybrid control strategy was proposed, which the controller parameters were optimized by APSO on multi-model of control system. Cascade control, feedforward contol and optimization were integrated in the new control method to avoid pressure fluctuation and parameter re-tuning. The outer loop was adopted fuzzy controller, inner loop adopted PID controller, grouting density is tuned by fuzzy feedforward method on the purpose of saving the project cost. controller parameters are optimized with APSO. Simulation results show that the control strategy obtain good tracking control effect in different stratum environment.
4.Hardware system and software system of control system were firstly developed to realize the pressure automatic control.
Research on the high-precision measurement ways of grouting pressure, flow and density parameters of control system, and design pressure sensor with high-pressure plastic isolator and non-contact density detector, which provide accurate data for control system. The algorithm above was firstly used to the grouting control experimental system, the results demonstrate the control strategy is superior to manual control ways in control accuray, stability and antijamming capability.
引文
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