遗传自适应模糊控制器的研究

主要研究了一种基于遗传算法的自适应模糊控制器的实现与性能分析。利用遗传算法的整体寻优性能对三维模糊控制器的调整因子进行优化组合,来达到对复杂被控对象的最佳控制效果。从实验数据结果分析表明,经遗传算法优化后的控制参数作用于模糊控制器,将使该控制器具有良好的动静态控制指标。最后还采用８０９８单片机实现了模糊自适应遗传控制器对窑炉炉温的控制,并取得了良好的效果。     

双线性约束过程的鲁棒自适应数据校正方法

采用污染正态分布模型进行数据校正，相对于传统的最小二乘方法具有较好的鲁棒性，然而参数估计结果的精确度依赖于误差发生概率和方差比值两个先验模型参数的选取，这在实际生产中难以获得，采用固定的方差比也不符合实际，因而其应用受到了限制。本文针对污染正态分布模型的不足，提出了一种鲁棒自适应误差分布模型，该模型具有与标准正态分布模型相似的分布密度函数，不同之处在于采用鲁棒自适应可变权重因子调节误差方差，通过放大显著误差方差，减小其对参数估计的影响。将该模型用于双线性约束数据校正问题，并采用Lagrange乘子法得到鲁棒自适应最小二乘分析解，同时还对鲁棒自适应数据校正中的测量数据相关性问题进行了研究。仿真结果证实了该方法的有效性。     

智能算法在交流调速系统中的研究

提出一种基于遗传算法优化的异步电机参考模型模糊自适应矢量控制调速系统,其异步电机模糊自适应速度辨识机构可根据参考模型输出与被控可调模型输出之差及其变化率,产生一个模糊自适应信号,控制被控可调模型的输出趋于参考模型的输出.仿真和实验表明,该系统具有良好的动态特性,在稳态精度上也优于普通的模型参考自适应系统,为无速度传感器...     

基于遗传算法的K型热电偶非线性校正

针对直接用遗传算法对K型热电偶进行曲线拟合出现的不足,如收敛过早及局部搜索能力效果差等,采用基于遗传算法的逐步逼近来拟合。考虑到K型热电偶非线性程度不高,提出一种改进遗传算法的非线性校正。经过3次不同适应度函数,同时对遗传算法中的交叉和变异操作进行适当的改进,拟合数据参考标准K型热电偶分度表。最终拟合出电压温度关系函数。通过对比不同方法的拟合结果,表明新方法能实现更好地进行非线性校正,且拟合精度高,相对误差控制在1%以下。     

化工过程数据校正方法的探讨

简要地介绍了数据校正中的基本概念；然后从数据协调、显著误差检测、数据校正三个方面较系统地、全面地总结了数据校正方法的研究进展和成就；最后探讨了这一领域中值得进一步研究的问题和可能的发展方向。     

恒温擦膜蒸发器的控制研究与实现

以废润滑油再生装置中的恒温擦膜蒸发器为研究对象,使用MATLAB中的辨识工具箱对模型进行了辨识,采用PID、DMC(动态矩阵控制)与模糊自适应PID 3种方法在SIMULINK中搭建控制模型.比较仿真结果可知:模糊自适应PID为该装置的最佳控制策略,为控制实现提供了理论基础.     

一种改进的非线性动态数据协调方法

在分析传统非线性动态数据校正方法的基础上,提出了一种改进的非线性动态数据协调方法.该方法利用滑动窗口法有效地降低了动态数据校正的维数,大大提高了系统运算的效率.同时引入快速的3σ法并修改了逻辑判断,在提高检测和剔除显著误差速度的同时,消除了传统数据协调算法的时滞性,动态数据协调的效果和效率都得到了提高.最后,通过对一典型动态系统CSTR的仿真实例说明了该方法的有效性.     

基于模糊参数自校正技术的气举压缩机排量控制

介绍了一种用于气举压缩机宽工况下的自适应排量控制方法。利用模糊控制技术实现PID控制器的参数在线自校正,提升了控制器性能。     

化工过程中的数据协调及显著误差检测

测量数据中不可避免地会含有随机性误差及显误差。本在综合大量献的基础上，从数据协调及显误差检测两个方面系统，全面地介绍和比较了数据校正的有关方法，并对非线性及动态数据校正进行了讨论。     

免疫遗传算法在胶乳聚合反应釜温度控制中的应用

提出一种免疫遗传算法优化的模糊神经元控制器,利用免疫遗传算法的全局搜索功能和神经元的自学习能力,提高了模糊控制器的控制精度和抗干扰能力.将该控制器用于胶乳聚合反应釜的温度控制,仿真结果表明该控制器可以有效地消除静态误差,并对聚合反应生产过程中的温度骤变有很好的鲁棒性,实际应用效果也表明了该方法的优越性.     

基于约束强跟踪滤波器的动态数据协调

引言 在实际工业过程中,由于过程测量数据的不平衡性和不完备性,给过程分析和研究工作带来了很多困难,甚至失败.因此必须对过程数据进行校正,然而目前的数据校正方法大部分是面对稳态过程的,但实际情况中过程的条件更多地是处在变化之中,此时稳态数据校正方法已不能满足要求.     

含反应节点过程数据校正的扩展独立物流法

Among the techniques developed for bilinear data reconciliation problems, the method based on independent flows is well known in terms of both accuracy and efficiency. However, the independent flow method is not effective when reactor units are present in the process. In this paper, an extended independent flow method is proposed for the data reconciliation of the process with chemical reaction. By the new method, the independent flows finding algorithm is adjusted to avoid the difficulties caused by the reactors in the process, and the reaction constraints are introduced into the objective function of data reconciliation. As a result, the new method can be applied to the process with chemical reaction while retaining high solution accuracy. To test the performance, the new method and the most typical Crowe‘s projection method are used in the data reconciliation of a typical industrial process. The results show that the new method can effectively accomplish the data reconciliation of the muhicomponent process with chemical reaction and gives more accurate estimates than the Crowe‘s method.     

A new method of redundancy analysis in data reconciliation and its application

In data reconciliation, it is common practice to detect gross errors and delete those measurements with gross error (MGEs) before using reconciliation algorithm. Since some MGEs’ deletion will perhaps result in unreasonable solution of the least squares algorithm of data reconciliation, MGEs cannot be deleted totally at one time. They must be treated respectively in order to delete as many MGEs as possible to ensure the reconciliation precision. To solve this problem, a new method of redundancy analysis is proposed in this paper. It also gives suggestion on instruments checking. Simulation results demonstrate the efficiency and consistency of the proposed method. And the application to a refinery is also discussed.     

一种新的双线性数据协调方法

在流程工业数据校正中,若涉及多组分物料平衡或能量平衡时,问题则转换为一类特殊的非线性问题,即双线性数据协调.今针对双线性数据协调传统方法的不足,给出了一种新的方法.首先提出了一种消除不可观测变量的方法,通过消除不可观测变量及部分非冗余变量将协调问题降维,并将问题分解为两个子问题;然后针对分解后的子问题,利用微粒群优化算法(PSO)求解.与传统方法相比,该方法在确保高协调精度的基础上具有较好的协调运算效率,并能处理过程中含有不可观测变量的情况.今对一个实例进行了仿真,仿真结果表明该方法的有效性.     

数据协调中冗余性分析新方法

提出一种数据协调中的冗余性分析新方法 .根据尽量删除带有显著误差的变量 ,同时确保协调精度的原则 ,对所有检测出来的带有显著误差的变量 ,通过计算不同阶次的冗余度 ,按照冗余度的大小逐个选择可以删除的变量 .这样既充分利用了数据信息 ,又确保了协调精度 .仿真结果证实了该方法的有效性 .     

数据协调中冗余性分析新方法

提出一种数据协调中的冗余性分析新方法 .根据尽量删除带有显著误差的变量 ,同时确保协调精度的原则 ,对所有检测出来的带有显著误差的变量 ,通过计算不同阶次的冗余度 ,按照冗余度的大小逐个选择可以删除的变量 .这样既充分利用了数据信息 ,又确保了协调精度 .仿真结果证实了该方法的有效性 .     

一种混杂系统数据校正新方法

对于既包含连续生产过程又包含离散事件的混杂系统,尤其是对于带有生产方案切换的实际生产过程,通过在物料平衡模型中引入随机调度方程,从而构造出包含随机调度方程参数变量θ的新型协调模型,然后利用一种不确定模型的协调算法对此模型进行求解,最后,通过仿真研究证实了该方法的有效性和鲁棒性.     

Data reconciliation strategy with time registration for the evaporation process in alumina production

In real industrial production, many mass and heat transfer processes are influenced by high temperature, high pressure, and even strong acid or alkali conditions. In addition, some important variables cannot be measured and chemical compositions are analyzed offline with a long time delay, which leads to inaccurate measurements of the process data. In this paper, a layered data reconciliation (LDR) method based on time registration is proposed to improve the measurement accuracy and estimate unmeasured variables. Considering that the material cannot be tagged and tracked in process manufacturing, a temporal and spatial matching strategy for the process data is designed based on a time‐correlation analysis matrix which is determined to describe the correlation of each time sequence in the data matrix. Then, a layered data reconciliation model with time registration is developed by reconciling the mass balance layer and the heat balance layer separately and stepwise, and the model is solved by the state transition algorithm. Meanwhile, regular terms and engineer's knowledge are introduced into the data reconciliation model to solve the problem of insufficient redundancy. The industrial verification results from the actual industrial evaporation process indicate that the accuracy of measured values is improved by using the proposed reconciliation strategy.     

一种改进的动态数据校正离群值检测法

Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation （DDR） is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor （CSTR） process verifies the effectiveness of the proposed algorithm.     

一种新颖的鲁棒动态数据校正方法

Outlier in one variable will smear the estimation of other measurements in data reconciliation （DR）. In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influence of outliers on the result of DR. This method introduces a penalty function matrix in a conventional least-square objective function, to assign small weights for outliers and large weights for normal measurements. To avoid the loss of data information, element-wise Mahalanobis distance is proposed, as an improvement on vector-wise distance, to construct a penalty function matrix. The correlation of measurement error is also considered in this article. The method introduces the robust statistical theory into conventional least square estimator by constructing the penalty weight matrix and gets not only good robustness but also simple calculation. Simulation of a continuous stirred tank reactor, verifies the effectiveness of the proposed algorithm.