【作者】 钱丽；

【导师】 李大字；

【作者基本信息】 北京化工大学 ， 控制科学与工程， 2010， 硕士

【摘要】 论文一方面从解决顺丁橡胶聚合过程中门尼粘度在线测量问题出发,提出采用软测量技术来建立门尼粘度软仪表。通过对顺丁橡胶聚合过程中的工艺流程进行分析研究后,提出一种基于EGK’M-RBF网络的软测量建模方法,并利用顺丁橡胶聚合过程现场数据,建立了基于EGK’M-RBF网络的门尼粘度软测量模型。另一方面从解决复杂过程的控制问题出发,提出采用强化学习理论来进行控制。通过对酿酒酵母发酵过程以及强化学习控制理论进行研究,提出一种改进的多步行为Q学习控制算法,并将该算法应用到酿酒酵母发酵过程控制中。论文的主要研究内容和研究成果包括：1.首先对软测量和强化学习技术进行了概述,接着介绍了基于RBF网络的软测量技术的基本理论,并分析了RBF网络中心选取算法的优缺点以及采用PCA和KPCA进行非线性特征提取实现辅助变量选择的优缺点。2.通过分析,提出一种增强的全局K’-means聚类算法,并将该算法成功应用于高斯数据集和实际数据集的分类过程中。通过与改进的全局K-means算法以及K’-means算法进行比较,实际数据的实验结果证明所提出的算法能获得更好的聚类结果。接着将增强的全局K’-means聚类算法成功应用于RBF网络隐含层结构的确定,最后提出一种基于EGK’M-RBF网络的软测量建模方法,该方法采用本文提出的EGK’M来确定RBF网络隐含层的结构,采用KPCA实现辅助变量的二次选择,并给出基于EGK’M-RBF神经网络的在线自校正模型算法步骤。3.提出采用EGK’M-RBF神经网络建立顺丁橡胶门尼粘度的软测量模型,通过对模型结果的比较分析,可知EGK’M-RBF神经网络在门尼粘度软测量建模中具有优势。与其他三种模型相比,本文所提出的模型具有更好的拟合效果,更强的预测能力,更小的泛化误差。同时还对PCA和KPCA进行非线性特征提取进行了比较分析,可知KPCA更适合非线性特征提取。最后还简单介绍了顺丁橡胶门尼粘度软测量软件包的开发以及界面的设计。4.通过对强化学习控制算法以及酿酒酵母发酵过程进行研究,提出一种改进的多步行为Q学习控制算法,该算法由多步行为Q学习算法和一个模糊控制增益参数选择器组成。通过这种模糊控制增益参数选择器来自适应地选择控制增益参数,一方面加快控制跟踪的速度,另一方面有助于减少控制器的超调。实验结果证明,改进的多步行为Q学习控制器具有超调量小、跟踪速度快,过渡时间短,控制作用平稳等特点。更多还原

【Abstract】 Firstly, to solve the problem of on-line measurement of mooney viscosity of polybutadiene rubber, a soft sensor of mooney viscosity was proposed by using soft-sensing technology. Through analysis and researches on the process of polybutadiene rubber, an modeling method by radial basis function(RBF) network based on enhanced global K’-means algorithm(EGK’M) was presented, a soft-sensor model of mooney viscosity based on EGK’M-RBF network was established using field data of the process of polybutadiene rubberSecondly, in order to tackle the control problem of complex industrial process, a method for complex industrial process control using reinforcement learning algorithm was proposed. Through research on the process of Saccharomyces cerevisiae fermentation and reinforcement learning algorithm control theory, an improved multi-step action Q-learning control algorithm is presented. Algorithm was developed to control the ethanol concentration of the Saccharomyces cerevisiae fermentation process. Main contributions of the thesis are as follows:1. At the beginning, overviews are made both on soft sensing technology and reinforcement learning technology. Then, principle theory of soft sensing based on RBF network was introduced, including the advantages and disadvantages of RBF network center selection algorithms, the advantages and disadvantages of PCA and KPCA for extracting non-linear feature information and achieving selection of auxiliary variable.2. An enhanced global K’-means clustering algorithm is presented, and it had been developed for clustering Gaussian datasets and several actual datasets. The clustering results of the actual datasets demonstrate that the enhanced global K’-means algorithm can get better clustering results compared to the modified global K-means algorithm and K’-means algorithm, respectively. Then, the enhanced global K’-means algorithm was applied to determine the structure of the hidden layer of RBF network. A modeling method by radial basis function (RBF) network based on enhanced global K’-means algorithm (EGK’M) was presented. In the proposed method the structure of RBF network was detrmined through EGK’M algorithm, KPCA algorithm was used for non-linear feature information and secondary selection of auxiliary variable. Finally, in the thesis, a series procedure of on-line self-calibration model algorithm which based on EGK’M-RBF network was given.3. Modeling of mooney viscosity of polybutadiene rubber with EGK’M-RBF network was proposed. From the comparative analysis of the modeling results, one can see that advantages of EGK’M-RBF network lies in that the model proposed much better fitting results, stronger predictive ability, smaller absolute error. At the same time, comparative analysis on PCA and KPCA for extracting non-linear feature information shows that KPCA is more suitable for non-linear feature extraction. Finally, a brief introduction to the development and interface design of soft-sensing software package of Mooney viscosity of polybutadiene rubber was given.4. An improved multi-step action Q-learning control algorithm was proposed for the process of Saccharomyces cerevisiae fermentation, which combines multi-step action Q-learning algorithm and a fuzzy control gain parameter selector. The fuzzy control gain parameter selector was used to adaptively select the control gain parameter, it can lead to faster tracking and help to alleviate the overshoot of controller. Experiment results show that the improved multi-step action Q-learning controller has much lower overshoot, faster tracking, shorter transition, and smoother control signal and so on.更多还原