基于模糊混沌的板形识别与控制技术的研究
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摘要
随着我国对板带钢材数量和种类增加的同时对板带钢材产品质量的要求也日益提高,板形测控技术是提高板形质量的关键。目前板形测控技术已经成为现代化高精度板带轧机的研究热点,而板形信号识别和板形先进控制方法与策略的研究是其重要发展方向。本文以模糊技术、混沌算法和广义预测控制理论为基础,对板形测控技术进行了研究,主要工作如下:
针对板形信号模式的传统识别方法、模糊识别方法和神经网络识别等方法的优缺点,提出了基于模糊混沌的板形信号识别方法。该方法采用模糊识别作为初步识别,用以降低混沌优化的求解维数和缩小搜索空间,而后采用混沌优化对其进行进一步识别,构建了两步寻优模糊混沌板形信号识别,该识别方法既具有模糊识别的简单、快速和稳定性的优点,同时又具有较高的精度。
在提出的两步寻优识别方法中,对混沌优化算法进行了分析与研究,针对混沌优化算法的局部搜索能力差的不足,借助梯度下降法的思想,给出一种利用先验知识和局部搜索相结合的新方法;对混沌搜索范围进行划分,建立一种对应新的映射优化变量取值搜索区域。
对模糊预测技术的应用进行了研究,提出了基于模糊辨识方法的轧制力模糊预测模型。采用三角形隶属函数和聚类型隶属函数进行模糊模型前提参数的辨识,结论参数辨识采用加权递推最小二乘算法(WRLSA),经过对大量实际生产轧制数据的仿真实验,证明了该模型性能稳定,运行速度快,训练样本较少,从而为轧制力的预测开辟了一条新的途径。
液压弯辊控制是板形测控系统的最基本环节,它的动态特性和稳态性能对于整个板形控制系统的性能起着至关重要的作用。针对液压弯辊控制具有非线性、时变性及不确定性等特性,及其对抗干扰性的要求,在分析其控制机理的基础上,提出了一种基于广义预测控制的液压弯辊控制方案,拓展了先进控制理论在板带轧制过程控制中的应用。
研究了广义预测控制算法,充分发挥广义预测控制算法的可塑性,给出了输出误差预报补偿的实时校正新算法,主要包括:采用实时性较强的时间序列预报AR模型对系统未来时刻误差进行预报并补偿,以减小模型误差和系统扰动的影响;推出了一种新的简化递推优化算法,以解决广义预测控制算法计算量大而影响实时性的问题;将输出量也作为滚动优化目标项目之一,和将系统输出量采用当前与未来预测控制量的加权均值算法,从而实现了系统快速性、小超调和稳定性。
With the expanding demands of plate/sheet output and quality, the technique of flatness measurement and control has been the research key of modern high precise plate/sheet rolling mill. It is important investigation aspects for Signal recognition, advanced control method and strategies of flatness. In this study, the system of flatness measurement and control has been researched based on fuzzy technique, chaotic optimization and Generalized Predictive Control (GPC). Main works are as follows:
For the advance and absence of traditional recognition method, fuzzy recognition method and neural network recognition method for flatness signal pattern, a fuzzy chaotic recognition method is presented firstly. There are two stages in Signal recognition pattern of flatness. Firstly, fuzzy recognition as preliminary one to decrease the solution dimension and reduce search band is adopted. Subsequently, chaotic optimization is used to further recognize. The recognition method has many merits, such as simplification, fast and stability, high accuracy.
In two stages optimization recognite method, Based on the analysis and investigation of the chaotic optimization algorithm, a span of mapping optimization vriable has been built-up to divide chaotic searching range; For the chaotic optimization has low ability in the aspect of searching local range, a new way, combining the prior knowledge with the local search, has been developed to improve the searching ability with the aid of gradient decrease idea.
A fuzzy prediction model of rolling force has been established based on the fuzzy recognition theory. In this report, the triangle subjection function and the clustering subjection function are employed to recognize precondition parameters of the fuzzy model. And weighting recursion least-squares algorithm (WRLSA) is employed to recognize inclusion parameters. Simulation experiment show that this model has the characteristics of high prediction accuracy and high operation speed.
Due to non-linearity, time-variation and non-determinacy of hydraulic roll-bending control, as well the anti-interference request, a hydraulic roll-bending control scheme has been put forward based on GPC after analysis of system mathematical models. Widening the using of the advanced control theory apply to plate/sheet rolling mill control.
GPC has been sturdied, the compensated GPC model with predicting output error has been set-up. The time series AR model with the high real-time property can predict and compensate the system future time error; A new simplified recursion optimization algorithm has been developed to solve the GPC real-time problems caused by the amount calculation; The GPC algorithm is improved to decrease overshoot, namely, the output parameter replaced with weighting average variable both predicted current and future controlled variable. Meanawhile, the output parameter is also one of rolling optimization objects to decrease system overshoot effectively. The system dynamic response speed and steady control quality has been realized.
针对板形信号模式的传统识别方法、模糊识别方法和神经网络识别等方法的优缺点,提出了基于模糊混沌的板形信号识别方法。该方法采用模糊识别作为初步识别,用以降低混沌优化的求解维数和缩小搜索空间,而后采用混沌优化对其进行进一步识别,构建了两步寻优模糊混沌板形信号识别,该识别方法既具有模糊识别的简单、快速和稳定性的优点,同时又具有较高的精度。
在提出的两步寻优识别方法中,对混沌优化算法进行了分析与研究,针对混沌优化算法的局部搜索能力差的不足,借助梯度下降法的思想,给出一种利用先验知识和局部搜索相结合的新方法;对混沌搜索范围进行划分,建立一种对应新的映射优化变量取值搜索区域。
对模糊预测技术的应用进行了研究,提出了基于模糊辨识方法的轧制力模糊预测模型。采用三角形隶属函数和聚类型隶属函数进行模糊模型前提参数的辨识,结论参数辨识采用加权递推最小二乘算法(WRLSA),经过对大量实际生产轧制数据的仿真实验,证明了该模型性能稳定,运行速度快,训练样本较少,从而为轧制力的预测开辟了一条新的途径。
液压弯辊控制是板形测控系统的最基本环节,它的动态特性和稳态性能对于整个板形控制系统的性能起着至关重要的作用。针对液压弯辊控制具有非线性、时变性及不确定性等特性,及其对抗干扰性的要求,在分析其控制机理的基础上,提出了一种基于广义预测控制的液压弯辊控制方案,拓展了先进控制理论在板带轧制过程控制中的应用。
研究了广义预测控制算法,充分发挥广义预测控制算法的可塑性,给出了输出误差预报补偿的实时校正新算法,主要包括:采用实时性较强的时间序列预报AR模型对系统未来时刻误差进行预报并补偿,以减小模型误差和系统扰动的影响;推出了一种新的简化递推优化算法,以解决广义预测控制算法计算量大而影响实时性的问题;将输出量也作为滚动优化目标项目之一,和将系统输出量采用当前与未来预测控制量的加权均值算法,从而实现了系统快速性、小超调和稳定性。
With the expanding demands of plate/sheet output and quality, the technique of flatness measurement and control has been the research key of modern high precise plate/sheet rolling mill. It is important investigation aspects for Signal recognition, advanced control method and strategies of flatness. In this study, the system of flatness measurement and control has been researched based on fuzzy technique, chaotic optimization and Generalized Predictive Control (GPC). Main works are as follows:
For the advance and absence of traditional recognition method, fuzzy recognition method and neural network recognition method for flatness signal pattern, a fuzzy chaotic recognition method is presented firstly. There are two stages in Signal recognition pattern of flatness. Firstly, fuzzy recognition as preliminary one to decrease the solution dimension and reduce search band is adopted. Subsequently, chaotic optimization is used to further recognize. The recognition method has many merits, such as simplification, fast and stability, high accuracy.
In two stages optimization recognite method, Based on the analysis and investigation of the chaotic optimization algorithm, a span of mapping optimization vriable has been built-up to divide chaotic searching range; For the chaotic optimization has low ability in the aspect of searching local range, a new way, combining the prior knowledge with the local search, has been developed to improve the searching ability with the aid of gradient decrease idea.
A fuzzy prediction model of rolling force has been established based on the fuzzy recognition theory. In this report, the triangle subjection function and the clustering subjection function are employed to recognize precondition parameters of the fuzzy model. And weighting recursion least-squares algorithm (WRLSA) is employed to recognize inclusion parameters. Simulation experiment show that this model has the characteristics of high prediction accuracy and high operation speed.
Due to non-linearity, time-variation and non-determinacy of hydraulic roll-bending control, as well the anti-interference request, a hydraulic roll-bending control scheme has been put forward based on GPC after analysis of system mathematical models. Widening the using of the advanced control theory apply to plate/sheet rolling mill control.
GPC has been sturdied, the compensated GPC model with predicting output error has been set-up. The time series AR model with the high real-time property can predict and compensate the system future time error; A new simplified recursion optimization algorithm has been developed to solve the GPC real-time problems caused by the amount calculation; The GPC algorithm is improved to decrease overshoot, namely, the output parameter replaced with weighting average variable both predicted current and future controlled variable. Meanawhile, the output parameter is also one of rolling optimization objects to decrease system overshoot effectively. The system dynamic response speed and steady control quality has been realized.
引文
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