钛合金棒材连轧过程的智能优化控制方法研究
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摘要
本文以钛合金棒材连轧生产线为工程背景,总结和分析了棒材轧制技术的特点,轧制方式和智能优化控制技术在轧制领域的应用,并对轧制过程的模型进行了深入的研究。由于国内外用户对棒材产品质量要求越来越高,棒材连轧的精确控制就显得尤为重要。本文重点研究了基于NMPSO算法的粗轧机速度优化控制和速度补偿策略,研究了基于ISTPSO优化的中轧机和精轧机张力模糊控制,研究了基于ACPSO优化SVR的棒材轧制力预测算法和轧制过程的故障诊断等关键技术,并应用于实际轧制,效果良好。主要研究内容分为以下几个方面：
(1)结合现场实际生产条件,给出了钛合金棒材连轧系统的组成和总体结构框图,研究了轧制过程的数学模型,包括：直流电动机的模型,张力模型,轧制力模型,变形抗力模型和温度模型等。
(2)针对粗轧机速度不协调而引起的中轧机入口尺寸波动问题,提出了基于非均匀动态变异粒子群(]NMPSO)优化粗轧机速度PID算法及补偿策略。首先研究了粒子群算法和非均匀动态变异粒子群算法的原理,然后应用Rosenbrock函数和Rastrigrin函数对NMPSO算法进行了测试,测试结果显示NMPSO算法收敛速度快于标准粒子群算法,最后给出了粗轧机速度输出及负载扰动时的SIMULINK仿真模型,并将该模型与粒子群算法相结合应用于轧件咬入轧辊时的速度补偿仿真和实验研究,仿真结果和实验证明经过优化和补偿的速度控制策略能够克服棒材咬入时的速度跌落,使得粗轧轧制过程平稳,顺畅。
(3)针对中轧机和精轧机由于张力控制不协调引起的堆料及拉料现象,提出了基于改进的自协调粒子群(ISTPSO)优化的中精轧机张力模糊PID控制算法。由于中轧机和精轧机的轧制速度快,且速度与张力相互耦合,不容易建立精确的数学模型,而模糊控制算法不依赖于被控对象的精确数学模型,鲁棒性和适应性好,因此本文提出应用模糊算法优化中精轧机张力控制器。为了克服实际控制中来自被控对象一定程度的波动所导致的控制系统难以取得较高精度的现象,本文提出了改进的自协调粒子群优化模糊控制器的量化因子和比例因子的算法,并配合提出的张力软测量方法,把算法应用在多机架连轧系统,仿真及实验结果验证了所提方法的有效性,解决了中轧机由于张力波动引起的实际堆料及拉料现象。
(4)针对钛合金棒材热连轧轧制力的精确预测问题,提出了一种基于加速收敛的粒子群优化支持向量回归机的预测算法。该算法首先通过使粒子在每次速度迭代过程中偏离速度迭代一个小角度,在位置迭代过程中偏离迭代位置一小步,改善了粒子群算法的收敛性及收敛速度,再通过ACPSO算法实现对支持向量回归机的参数ε,c,γ的同时寻优,从而使ACPSO-SVR模型具有较高的预测精度和泛化能力。通过仿真实验和实际数据的比对,验证了方法的有效性。实验结果表明,ACPSO-SVR方法能够有效,快速的实现轧制力的精确预测,在预测速度和适应性方面,优于基于PSO-SVR的预测方法；在预测精度方面,该方法优于BPNN、SVR、PSO-SVR等方法,平均误差率从BP神经网络的±9%降到±4%以内。
(5)在钛合金棒材连轧轧制过程的应用背景下,结合提升小波数据降噪方法,提出了LW-RLSSVM、LW-PNN两种轧制过程故障诊断改进方法。基于现场数据的仿真实验结果验证了所提方法的有效性,全面提升了棒材连轧系统优化控制的可靠性。
本文的研究内容,面向钛合金棒材连轧轧制过程的实际控制,具有很强的实用性。本文的研究结论在生产现场得到了验证,提高了棒材产品的质量。
This thesis is based on the project of the titanium alloy rod rolling line. The characteristics of bar rolling technology, rolling method, the application in the field of intelligent optimization control technologies and the model of the rolling process are summarized and analyzed. Due to the increasingly high demand for domestic and foreign users of the product quality, accurate control of bar rolling is particularly important. Main research methods of this thesis include speed optimization control and speed compensation of Roughing mill based on NMPSO algorithm, tension fuzzy optimization control of intermediate mill and finishing mill, bar rolling force prediction algorithm based on ACPSO optimized SVR and fault diagnosis of rolling process. The main research methods are used to actual rolling and have achieved good results. The main research contents and conclusions are as follows:
(1) Combined with the actual production condition, the composition of titanium alloy rod rolling system and the framework of overall structure are studied. The mathematical model of rolling process included dc motor model, tension model, rolling force model, deformation resistance model and the temperature model.
(2) Aiming at the size fluctuation problem of the inlet of intermediate mill causing by rough mill's speed mismatch, an optimization algorithm based on NMPSO optimized speed PID and compensation strategy are proposed. Firstly, the principle of PSO algorithm and non-uniform dynamic mutation particle swarm optimization algorithm are studied, and then the Rosenbrock function and Rastrigrin function are tested. Test result shows that NMPSO algorithm convergence speed is faster than the standard PSO algorithm. The simulink model of Roughing mill speed output and load disturbances is presented finally. The model combined with NMPSO algorithm is applied to roll speed compensation research. Simulation results and experimental showed that optimization and compensation strategy of speed can overcome the speed drop and which can make rough rolling process smooth.
(3) Aiming at the heap materials and pull material phenomenon caused by tension's mismatch, an optimization algorithm based on ISTPSO optimized fuzzy tension PID control algorithm is proposed. Due to the mutual coupling of speed and tension, it is not easy to establish accurate mathematical model. Fuzzy control algorithm does not rely on accurate mathematical model of controlled object, and which has good robustness and adaptability, so tension controller optimized by fuzzy algorithm in the intermediate mill and finishing mill were used. In order to overcome the disturbance of the actual control, an1STPSO algorithm was proposed which can optimize the quantization factor and scaling factor. Combined with tension soft measurement method, the ISTPSO algorithm is applied to rolling system, the simulation and experimental results verify the effectiveness of the proposed method, and which solved the heap materials and pull material phenomenon caused by tension fluctuation.
(4) Aiming at the accurate prediction problem of titanium alloy bars rolling force, an optimal approach of support vector regression (SVR) parameters is proposed based on the accelerate convergence particle swarm optimization (ACPSO) algorithm. Firstly, this algorithm improve convergence and convergence speed through making particle's speed deviate from a small angle in each speed iterative process and making particle's position deviate from a small step in each position iterative process. Secondly, ACPSO algorithm is applied to optimize the three parameters synchronously, which make the ACPSO-SVR model good prediction accuracy and generalization capabilities. Through simulation experiment and particle date comparison, the validity of the method is validated. The results show that the ACPSO-SVR algorithm can effectively predict rolling force, and is superior to PSO-SVR at prediction speed and adaptability, and the ACPSO-SVR algorithm is better than BPNN, SVR and PSO-SVR in precision of prediction and the average error rate decreases from±9%achieved by the BP neural network to less than±4%by using ACPSO-SVR algorithm.
(5) Under the background of application of titanium alloy rod continuous rolling process, combined with lifting wavelet data noise reduction method, LW-RLSSVM algorithm and LW-PNN algorithm are put forward, which are used for the fault diagnosis of rolling process. Simulation results verify the validity of the proposed method, and enhance the reliability of bar rolling optimal control system.
The content of this research is focused on the control of titanium alloy rod rolling process, which has a strong practical. The research result in the thesis has been confirmed in the work field and the production is improved.
(1)结合现场实际生产条件,给出了钛合金棒材连轧系统的组成和总体结构框图,研究了轧制过程的数学模型,包括：直流电动机的模型,张力模型,轧制力模型,变形抗力模型和温度模型等。
(2)针对粗轧机速度不协调而引起的中轧机入口尺寸波动问题,提出了基于非均匀动态变异粒子群(]NMPSO)优化粗轧机速度PID算法及补偿策略。首先研究了粒子群算法和非均匀动态变异粒子群算法的原理,然后应用Rosenbrock函数和Rastrigrin函数对NMPSO算法进行了测试,测试结果显示NMPSO算法收敛速度快于标准粒子群算法,最后给出了粗轧机速度输出及负载扰动时的SIMULINK仿真模型,并将该模型与粒子群算法相结合应用于轧件咬入轧辊时的速度补偿仿真和实验研究,仿真结果和实验证明经过优化和补偿的速度控制策略能够克服棒材咬入时的速度跌落,使得粗轧轧制过程平稳,顺畅。
(3)针对中轧机和精轧机由于张力控制不协调引起的堆料及拉料现象,提出了基于改进的自协调粒子群(ISTPSO)优化的中精轧机张力模糊PID控制算法。由于中轧机和精轧机的轧制速度快,且速度与张力相互耦合,不容易建立精确的数学模型,而模糊控制算法不依赖于被控对象的精确数学模型,鲁棒性和适应性好,因此本文提出应用模糊算法优化中精轧机张力控制器。为了克服实际控制中来自被控对象一定程度的波动所导致的控制系统难以取得较高精度的现象,本文提出了改进的自协调粒子群优化模糊控制器的量化因子和比例因子的算法,并配合提出的张力软测量方法,把算法应用在多机架连轧系统,仿真及实验结果验证了所提方法的有效性,解决了中轧机由于张力波动引起的实际堆料及拉料现象。
(4)针对钛合金棒材热连轧轧制力的精确预测问题,提出了一种基于加速收敛的粒子群优化支持向量回归机的预测算法。该算法首先通过使粒子在每次速度迭代过程中偏离速度迭代一个小角度,在位置迭代过程中偏离迭代位置一小步,改善了粒子群算法的收敛性及收敛速度,再通过ACPSO算法实现对支持向量回归机的参数ε,c,γ的同时寻优,从而使ACPSO-SVR模型具有较高的预测精度和泛化能力。通过仿真实验和实际数据的比对,验证了方法的有效性。实验结果表明,ACPSO-SVR方法能够有效,快速的实现轧制力的精确预测,在预测速度和适应性方面,优于基于PSO-SVR的预测方法；在预测精度方面,该方法优于BPNN、SVR、PSO-SVR等方法,平均误差率从BP神经网络的±9%降到±4%以内。
(5)在钛合金棒材连轧轧制过程的应用背景下,结合提升小波数据降噪方法,提出了LW-RLSSVM、LW-PNN两种轧制过程故障诊断改进方法。基于现场数据的仿真实验结果验证了所提方法的有效性,全面提升了棒材连轧系统优化控制的可靠性。
本文的研究内容,面向钛合金棒材连轧轧制过程的实际控制,具有很强的实用性。本文的研究结论在生产现场得到了验证,提高了棒材产品的质量。
This thesis is based on the project of the titanium alloy rod rolling line. The characteristics of bar rolling technology, rolling method, the application in the field of intelligent optimization control technologies and the model of the rolling process are summarized and analyzed. Due to the increasingly high demand for domestic and foreign users of the product quality, accurate control of bar rolling is particularly important. Main research methods of this thesis include speed optimization control and speed compensation of Roughing mill based on NMPSO algorithm, tension fuzzy optimization control of intermediate mill and finishing mill, bar rolling force prediction algorithm based on ACPSO optimized SVR and fault diagnosis of rolling process. The main research methods are used to actual rolling and have achieved good results. The main research contents and conclusions are as follows:
(1) Combined with the actual production condition, the composition of titanium alloy rod rolling system and the framework of overall structure are studied. The mathematical model of rolling process included dc motor model, tension model, rolling force model, deformation resistance model and the temperature model.
(2) Aiming at the size fluctuation problem of the inlet of intermediate mill causing by rough mill's speed mismatch, an optimization algorithm based on NMPSO optimized speed PID and compensation strategy are proposed. Firstly, the principle of PSO algorithm and non-uniform dynamic mutation particle swarm optimization algorithm are studied, and then the Rosenbrock function and Rastrigrin function are tested. Test result shows that NMPSO algorithm convergence speed is faster than the standard PSO algorithm. The simulink model of Roughing mill speed output and load disturbances is presented finally. The model combined with NMPSO algorithm is applied to roll speed compensation research. Simulation results and experimental showed that optimization and compensation strategy of speed can overcome the speed drop and which can make rough rolling process smooth.
(3) Aiming at the heap materials and pull material phenomenon caused by tension's mismatch, an optimization algorithm based on ISTPSO optimized fuzzy tension PID control algorithm is proposed. Due to the mutual coupling of speed and tension, it is not easy to establish accurate mathematical model. Fuzzy control algorithm does not rely on accurate mathematical model of controlled object, and which has good robustness and adaptability, so tension controller optimized by fuzzy algorithm in the intermediate mill and finishing mill were used. In order to overcome the disturbance of the actual control, an1STPSO algorithm was proposed which can optimize the quantization factor and scaling factor. Combined with tension soft measurement method, the ISTPSO algorithm is applied to rolling system, the simulation and experimental results verify the effectiveness of the proposed method, and which solved the heap materials and pull material phenomenon caused by tension fluctuation.
(4) Aiming at the accurate prediction problem of titanium alloy bars rolling force, an optimal approach of support vector regression (SVR) parameters is proposed based on the accelerate convergence particle swarm optimization (ACPSO) algorithm. Firstly, this algorithm improve convergence and convergence speed through making particle's speed deviate from a small angle in each speed iterative process and making particle's position deviate from a small step in each position iterative process. Secondly, ACPSO algorithm is applied to optimize the three parameters synchronously, which make the ACPSO-SVR model good prediction accuracy and generalization capabilities. Through simulation experiment and particle date comparison, the validity of the method is validated. The results show that the ACPSO-SVR algorithm can effectively predict rolling force, and is superior to PSO-SVR at prediction speed and adaptability, and the ACPSO-SVR algorithm is better than BPNN, SVR and PSO-SVR in precision of prediction and the average error rate decreases from±9%achieved by the BP neural network to less than±4%by using ACPSO-SVR algorithm.
(5) Under the background of application of titanium alloy rod continuous rolling process, combined with lifting wavelet data noise reduction method, LW-RLSSVM algorithm and LW-PNN algorithm are put forward, which are used for the fault diagnosis of rolling process. Simulation results verify the validity of the proposed method, and enhance the reliability of bar rolling optimal control system.
The content of this research is focused on the control of titanium alloy rod rolling process, which has a strong practical. The research result in the thesis has been confirmed in the work field and the production is improved.
引文
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