铜转炉吹炼终点预报模型研究

铜转炉吹炼终点预报是转炉生产的一个重要环节,直接影响转炉的生产效益.其吹炼工艺相当复杂,影响生产因素众多且因素之间相互耦合,使得用来预测吹炼终点的模型极其复杂.针对这个问题,利用主元分析法将影响因素重组,在此基础上,提出一种基于遗传算法的Elman神经网络模型对铜转炉吹炼终点进行预测.仿真结果表明,建立的预报模型具有较高的自学习及泛化能力,预报结果具有较高的精度.     

PS转炉造渣过程的动态优化控制

建立了Pierce-Smith(PS)转炉造渣过程的非线性状态空问模型,并提出了基于生产质量指标反馈校正的铜锍造渣过程的动态优化控制方案.该方案首先基于最优模型求得最优控制律.为消除吹炼过程中的扰动以及其他不确定因素所带来的影响,再引入基于生产质量指标的反馈调整机制.其中反馈信息由软测量模犁根据进出转炉的物料计算得到,智能控制单元根据反馈的质量信息和期望的质量目标间的偏差对最优控制律进行补偿修正.并在该动态优化控制方案基础上,设计并开发了铜锍吹炼过程的优化控制指导决策系统.实际运行结果表明该系统优化了产品的质量,同时也实现了铜锍生产过程的节能.     

用面向对象的方法实现转炉吹炼控制模型

转炉炼钢过程控制的核心是吹炼终点控制模型，该模型又由三个子模型组成。本文提出了如何用面向对象的程序设计方法来实现它，以充分利用Ｏ－Ｏ方法的继承、封装及消息传递等优点。     

基于有限信息的铜吹炼动态过程智能集成建模

针对具有强动态变化特征但过程信息有限可知的铜转炉吹炼过程, 提出一种基于有限数据信息的吹炼动态过程智能集成建模方法. 依据冶金反应动力学原理, 建立了描述吹炼过程反应体系变化的非线性动力学模型; 引入动力学系数修正因子, 基于有限的数据信息和龙格–库塔公式, 构建了动力学系数修正因子的优化模型; 结合智能决策生成的典型样本集, 提出了基于微粒群算法和模式搜索法的混合智能算法确保有效获得最优修正因子, 最 终形成吹炼过程的动态模型. 用实际生产数据仿真实验, 模型预测的最大相对误差小于5%, 仿真结果验证了模型有效性.     

炼铜转炉吹炼终点的神经网络和自适应残差补偿组合预报模型

提出了基于改进的BP神经网络学习算法和自适应残差补偿算法的炼铜转炉吹炼终点组合预报模型. 利用某厂实际生产数据进行仿真运行的结果表明, 本文建立的模型具有较高的预报精度和较强的实用性, 可用于指导生产实践.     

微机在线应用中的实用通讯技术

<正> 随着计算机的迅速发展,控制系统也发生了很大的变化,逐渐从离线转为在线化,由单机控制发展到计算机分级控制。分级控制中最基本的要求就是计算机之间的数据通讯问题。以氧气顶吹转炉炼钢为例。此种炼钢技术在生产中已充分显示其重要作用。由于氧气顶吹转炉吹炼周期很短,必须准确地判断吹炼终点,提高终点的命中率,炉龄和质量。由自由炼钢转向科学炼钢,必须采用计算机进行控制和管理。计算机可根据静态/动态模型,按附加料模型,枪位模型来控制附加料的加人数量和时间,控制变枪和枪位并对检测到的过程信息进行修正,以达到终点目标有较高的命中率。在这个系统中,牵涉到在线检测和收集数据及对     

基于灰度差分统计的火焰图像纹理特征提取

转炉吹炼过程中,炉口火焰的纹理粗糙度与吹炼所处的阶段有密切的联系.在不同的吹炼阶段,火焰纹理呈现较明显的变化,因此该特征可以作为转炉终点识别系统的重要参数之一,目的是从火焰图像中提取出描述纹理复杂度特征的描述子,进而区分出不同的吹炼阶段.提出基于灰度差分统计的火焰纹粗糙度提取方法,首先对原始火焰图像做去噪、分割、灰度转换的预处理,然后计算灰度差分直方图,在此基础上对比各种特征描述子表述粗糙度的效果.结果表明,和灰度共生矩阵方法的能量特征值相比,灰度差分统计方法中的角度方向二阶矩描述子对不同纹理所处的吹炼时期具有较高的识别率,且具有较快的识别速度,该特征描述子能作为重要的特征参数之一用于实际的转炉吹炼终点判断识别系统中.     

基于多任务学习的炼钢终点预测方法

钢水质量通常根据终点命中率来判断,但炼钢过程影响因素众多,机理分析难以准确预测终点温度和含碳量,鉴于此,提出一种由数据驱动的多任务学习（MTL）炼钢终点预测方法。首先,分析并提取炼钢过程的输入和输出要素,结合炼钢两阶段吹炼特点选择多个子学习任务;其次,根据子任务与终点参数的相关性选择合适的子任务,提升终点预测的准确度并构建多任务学习模型,再对模型输出结果进行二次优化;最后,通过近端梯度算法对处理后的生产数据进行模型训练,获取多任务学习模型的过程参数。以某钢厂为案例,该方法相比神经网络在终点温度12℃误差范围内和终点含碳量0.01%误差内的准确度提升了10%,误差范围6℃和0.005%的预测准确度分别提升了11%和7%。实验结果表明,多任务学习在实际中能够提升终点预测的准确性。     

基于混合递阶遗传神经网络的转炉温度预报

针对转炉控制中对吹炼终点温度的控制问题,提出了基于混合递阶遗传RBF神经网络(HGA-RBF)的转炉炼钢终点温度预报模型.研究了RBF网络的特点,用递阶遗传算法克服了网络的结构和参数选择的随机性问题;并结合最小二乘法,提高了收敛速度.仿真结果表明,此算法在一定程度上提高了RBF网络的优化收敛速度和训练测试精度.某钢铁公司提供的实际冶炼数据试验,也证明了该模型预报精度较高,对提高生产的质量有重要意义.     

一种DDMCN火焰图像特征提取的转炉炼钢碳含量实时预测方法

转炉炼钢终点控制作为吹炼末期重要操作的关键是碳含量准确实时预测,而熔池中碳含量的氧化速率能够反映在炉口火焰纹理变化上,因此提取火焰纹理的准确特征是终点碳含量预测的关键,但是火焰纹理具有多方向多尺度不规则的特征描述难点.鉴于此,提出一种导数非线性映射方向加权多层复杂网络彩色纹理描述符,符合火焰不规则纹理的多尺度多方向特点.首先,将HSI空间下火焰图像映射至相位空间以增强空间位置关联信息;然后,基于复杂网络给出一种反映不同尺度顶点间连续变化的导数关系权重公式,结合方向信息构建炉口火焰图像的多尺度不规则方向加权彩色纹理复杂网络;最后,计算顶点方向加权度特征量化复杂网络拓扑连接模式,构建火焰彩色纹理特征,建立KNN回归模型预测终点碳含量.实验结果表明,所提出算法满足实际转炉炼钢吹炼过程实时性要求.     

广义系统的终端滑模变结构控制

研究广义系统的终端滑模控制问题,通过线性奇异变换将广义系统化为受限等价形式,在此基础上给出相应的终端滑模超曲面和终端滑模控制律．研究结果表明,系统状态变量能以较快的收敛速度在有限时间内进入滑模超曲面并最终到达平衡点。     

有限时间快速收敛滑模变结构控制

讨论了一类非线性系统的滑模变结构控制有限时间收敛问题.提出了一种新的收敛滑模超曲面及相应控制方案.研究结果表明,系统状态变量能以较快的收敛速度在有限时间内进入各级滑模超曲面最终到达平衡点,并具有良好的动态性能.最后仿真结果验证了该方案的有效性.     

Adaptive Terminal ILC for Iteration‐varying Target Points

Terminal iterative learning control (TILC) has been developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations. In this work, the desired terminal point is not fixed but allowed to change run‐to‐run among a set of fixed points and a new adaptive terminal iterative learning control scheme is developed to achieve learning objective over iterations. The control signal is updated from the measured terminal value at the end of a run, instead of the whole output trajectory. Although the reference terminal point is iteration‐varying, the new adaptive TILC guarantees that the tracking error converges to zero iteratively. Both rigorous mathematical analysis and simulation results confirm the applicability and effectiveness of the proposed approach.     

非线性离散时间系统的最优终端迭代学习控制

仅利用系统的终端输出误差而不是整个输出轨迹,提出了一种最优终端迭代学习控制方法.控制信号可直接通过终点的误差信息进行更新.主要创新点在于控制器的设计和分析只利用系统量测的I/O数据而不需要关于系统模型的任何信息,并可实现沿迭代轴的单调收敛.在此意义上,所提出的控制器是数据驱动的无模型控制方法.严格的数学分析和仿真结果均表明了所提出方法的适用性和有效性.     

Optimal Terminal Iterative Learning Control for the Automatic Train Stop System

The train stop control is a typical set‐point control task, where only the final state (i.e., the terminal train stop position) is of concern and specified. For such a control problem, an optimal terminal iterative learning control (TILC) approach is presented in this paper, where the stopping position and initial braking speed are chosen as the terminal system output and the control input, respectively. The controller design only depends on the measured input/output (I/O) data without requiring any modeling information of the train operation system, and the learning gain is updated by the system I/O data iteratively to accommodate the system uncertainties. The monotonic convergence of the terminal tracking error is guaranteed by rigorous mathematical analysis. Extensive simulation results are provided to show the applicability and effectiveness of the proposed approach.     

Economic optimization using model predictive control with a terminal cost

In the standard model predictive control implementation, first a steady-state optimization yields the equilibrium point with minimal economic cost. Then, the deviation from the computed best steady state is chosen as the stage cost for the dynamic regulation problem. The computed best equilibrium point may not be the global minimum of the economic cost, and hence, choosing the economic cost as the stage cost for the dynamic regulation problem, rather than the deviation from the best steady state, offers potential for improving the economic performance of the system. It has been previously shown that the existing framework for MPC stability analysis, which addresses to the standard class of problems with a regulation objective, does not extend to economic MPC. Previous work on economic MPC developed new tools for stability analysis and identified sufficient conditions for asymptotic stability. These tools were developed for the terminal constraint MPC formulation, in which the system is stabilized by forcing the state to the best equilibrium point at the end of the horizon. In this work, we relax this constraint by imposing a region constraint on the terminal state instead of a point constraint, and adding a penalty on the terminal state to the regulator cost. We extend the stability analysis tools, developed for terminal constraint economic MPC, to the proposed formulation and establish that strict dissipativity is sufficient for guaranteeing asymptotic stability of the closed-loop system. We also show that the average closed-loop performance outperforms the best steady-state performance. For implementing the proposed formulation, a rigorous analysis for computing the appropriate terminal penalty and the terminal region is presented. A further extension, in which the terminal constraint is completely removed by modifying the regulator cost function, is also presented along with its stability analysis. Finally, an illustrative example is presented to demonstrate the differences between the terminal constraint and the proposed terminal penalty formulation.     

An alternate approach to the fixed terminal point regulator problem

The conventional calculus of variations solution of the fixed terminal point regulator problem implies the use of time-varying feedback in the controller. In systems with linear plants such a controller calls for gains that approach infinity as the time approaches the terminal time. An alternate, mathematically equivalent, solution is proposed in this paper. This solution expresses the optimum controller in terms of a sum of time-variant feedback (control law) and a forcing term (control function). Since the feedback gains remain constant and the control function is finite for all positive time, such a solution is physically realizable. The control law also guarantees asymptotic stability even after the terminal time has been reached.     

Comments on ‘A new terminal sliding mode control for robotic manipulators’

In this article, we give some comments on the article ‘A new terminal sliding mode control for robotic manipulators’. The article presents a new terminal sliding mode control approach for global finite-time tracking of robotic manipulators. We point out a serious error occurred through the article, leading to the ineffectiveness of the proposed approach. A correction is proposed. Comparisons are presented.     

Linear control theory applied to interplanetary guidance

This paper considers the problem of interplanetary guidance in the presence of random injection and measurement errors from the point of view of minimizing the expected value of the square of the control effort to meet a prescribed terminal accuracy. It is shown that the optimal control signal is a linear function of only those components of the predicted miss whose terminal covariance is specified. A one dimensional model which approximates the terminal phase of an interplanetary trip is considered in detail. Computer results are given showing the comparison of this solution with the minimum effort theory developed recently by Breakwell and Striebel [1] on the requirement for the expected amount of velocity corrections. An analytical expression is given which shows that the present design is about 13 per cent more costly in so far as the terminal portion of the expected velocity requirement is concerned.     

Stabilizing receding horizon H∞ controls forlinear continuous time-varying systems

In this note, new matrix inequality conditions on the terminal weighting matrices are proposed for linear continuous time-varying systems. Under these conditions, nonincreasing and nondecreasing monotonicities of the saddle point value of a dynamic game are shown to be guaranteed. It is proved that the proposed terminal inequality conditions ensure the closed-loop stability of the receding horizon H _∞ control (RHHC). The stabilizing RHHC guarantees the H _∞ norm bound of the closed-loop system. The proposed terminal inequality conditions for the monotonicity of the saddle point value and the closed-loop stability include most well-known existing terminal conditions as special cases. The results for time-invariant systems are obtained correspondingly from those in the time-varying case