单相六极电炉电极模糊控制系统的工程应用

综合分析了埋弧炉冶炼镍铁工艺的特点,找出真实反映电极插深的控制变量,利用模糊控制理论,提出了相应的电极调节算法,并在某项目的镍铁电炉设计中采用了电极调节模糊控制系统.实践证明,该控制系统较好地实现了电极自动调节的控制功能,满足了实际生产需要.     

基于减法聚类的带钢厚度数据驱动建模

针对轧钢生产中大批过程数据没有被用于提高厚度质量的现象,提出了一种基于减法聚类的带钢厚度数据驱动在线建模方法.首先通过减法聚类将输入空间划分为一些小的局部空间,在每个局部空间中用最小二乘支持向量机建立子模型,子模型加权输出作为带钢厚度的离线模型;然后当在线数据不断增加时,通过在线减法聚类算法实时调整局部空间,子模型的参数采用最小二乘支持向量机的递推算法进行相应的在线辨识,子模型的预测输出作为模型的最后输出.实验结果表明,该方法具有良好的预测精度和较强的在线学习能力.      

基于遗传算法的模糊控制理论研究及其应用

应用遗传算法和模糊控制的理论,针对一个随机双线性系统,通过MATLAB编程仿真,探讨一种新的优化模糊控制器的设计方法,即先离线优化隶属函数,后在线调整量化系数,使得系统逐步向超调小、响应快的方向发展.仿真结果表明了这种方法的有效性.     

基于遗传算法的模糊控制理论研究及其应用(续)

应用遗传算法和模糊控制的理论，针对一个随机双线性系统，通过MATLAB编程仿真，探讨一种新的优化模糊控制器的设计方法，即先离线优化隶属函数，后在线调整量化系数，使得系统逐步向超调小、响应快的方向发展。仿真结果表明了这种方法的有效性。     

一种基于卷积神经网络的CSI指纹室内定位方法

针对提高Wi-Fi指纹室内定位技术性能,提出了一种基于卷积神经网络（Convolutional neural networks,CNN）的信道状态信息（Channel state information,CSI）指纹室内定位方法。在离线阶段联合定位环境参考点的幅度差和相位差信息,利用CNN进行训练,保存训练后的CNN网络模型作为指纹;在线阶段,针对不同实验场景,对测试数据的幅度差信息和相位差信息进行加权处理,引入改进的基于概率的指纹匹配算法,利用待定位点的CSI信息并通过CNN网络模型预测待定位点的坐标。此外,为增强算法普适性,针对复杂室内场景,提出了双节点定位方案来提高定位精度。在廊厅和实验室室内两种不同定位场景进行了实验,信息联合定位算法分别获得了24.7 cm和48.1 cm的平均定位误差,验证了基于CNN的CSI幅度差和相位差联合定位算法的有效性。      

连续加热炉在线控制系统的仿真研究

以总括热吸收率法为基础,对连续加热炉在线控制过程进行了软件仿真研究.研究内容主要包括控制算法和炉子模型两部分,控制算法研究考虑了DDC和SCC两级控制,炉子仿真模型为离线研究提供了完善的软件平台.模型以能量平衡为基础,综合考虑了炉气、钢坯、炉墙和热电偶之间的传热,实现了炉气黑度随炉气温度的动态补偿.以实际的连续加热炉为研究对象,采用加热曲线法较全面地研究了炉子一般动态和典型大幅度动态操作时的SCC决策,得出了适于在线应用的优化控制策略.     

铝电解烟气净化除尘控制系统的开发与应用

介绍国内某大型铝冶炼企业设计开发的以S7-300系列PLC为控制器的铝电解烟气净化除尘控制系统,该系统控制方式先进,控制精度高;着重论述了关键子系统控制软、硬件的设计思路及其实现方法,并对该控制系统应用效果进行了分析。     

Adaptive Predictive Functional Control for Networked Control Systems with Random Delays

Nowadays, more and more field devices are connected to the central controller through a serial communication network such as fieldbus or industrial Ethernet.Some of these serial communication networks like controller area network (CAN) or industrial Ethernet will introduce random transfer delays into the networked control systcms (NCS), which causes control performance degradation and even system instability.To address this problem, the adaptive predictive functional control algorithm is derived by applying the concept of predictive functional control to a discrete state space model with variable delay.The method of estimating the networkinduced delay is also proposed to facilitate the control algorithm implementing.Then, an NCS simulation research based on TrueTime simulator is carried out to validate the proposed control algorithm. The numerical simulations show that the proposed adaptive predictive functional control algorithm is effective for NCS with random delays.     

广义预测控制算法在稀土萃取过程中的应用

针对稀土萃取过程中元素组分含量难以在线检测和实现自动控制的问题,提出通过建立多线性模型实现对元素组分含量进行软测量,并采用预测控制方法实现对稀土萃取过程组分含量的自动控制,仿真结果表明本文提出方法的有效性。     

基于数据挖掘的中厚钢板轧制负荷分配方法的研究

为了提高中厚钢板轧制负荷的预测精度，提出利用在线智能化信息处理技术进行建立轧制过程负荷分配模型的新方法。该方法采用知识发现(KDD)和数据挖掘(DM)对轧制运行数据库进行挖掘，建立知识模型和数学模型相结合的负荷分配模型。实现轧制过程负荷模型的在线维护和优化。离线模拟与在线应用证明了该方法的有效性。     

Online learning from finite training sets and robustness to input bias

We analyze online gradient descent learning from finite training sets at noninfinitesimal learning rates eta. Exact results are obtained for the time-dependent generalization error of a simple model system: a linear network with a large number of weights N, trained on p = alphaN examples. This allows us to study in detail the effects of finite training set size alpha on, for example, the optimal choice of learning rate eta. We also compare online and offline learning, for respective optimal settings of eta at given final learning time. Online learning turns out to be much more robust to input bias and actually outperforms offline learning when such bias is present; for unbiased inputs, online and offline learning perform almost equally well.     

多模型自适应控制理论及应用

作为对经典自适应控制改进的控制方法,多模型自适应控制是解决复杂的大范围参数不确定系统的一种有效途径,并在理论和实践中取得了丰富成果。依据控制器集的不同综合策略,其被分为多种类型,本文旨在对加权型的多模型自适应控制进行综述。加权多模型自适应控制的基本思路是采用“分而治之”的办法,离线建立多个局部模型和对应的多个局部控制器,在线加权融合各个局部控制器的控制输出,从而形成全局控制,是实现鲁棒自适应控制的一类重要方法。首先比较详细地介绍了加权多模型自适应控制研究的历史及现状,然后给出相关研究的新进展和一些观点,包括新的加权算法和相应的加权多模型自适应控制系统的稳定性结果等,最后指出未来的几个研究方向。      

Electrical optimization of superpowerful arc furnaces

Requirements on the systems controlling the electrical conditions of superpowerful arc furnaces in steel smelting are outlined. The basic principles for electrical optimization at different phases of smelting are considered. An improved mathematical model is proposed for analysis of the arc furnace’s electrical characteristics and the dynamics of the automatic control system governing electrode motion. A diagnostic system capable of identifying the phase of smelting on the basis of the harmonics of the arc currents shortens the furnace’s working cycle and reduces the power consumption.     

安钢FSF电极智能预测建模及应用研究

交流电弧炉电极控制系统是一个多变量、非线性、参数时变、复杂强耦合系统,经典的控制策略难以获得优良的性能.为此从安钢FSF电极控制的实际应用出发,提出了1种变结构遗传Elman网络预测建模方法,其中改进的混和遗传算法用来对网络结构和权值及自反馈增益的同步动态寻优.并将基于BP算法的改进Elman网络和本文提出的变结构遗传Elman网络都应用于交流电弧炉的电极模型的辨识中,通过基于安钢现场数据的计算机仿真实验表明:变结构遗传Elman网络克服了因复杂的辨识对象造成的网络辨识结构复杂问题和采用BP算法带来的权值训练缺陷;并具有更好的动态性能,逼近速度快,精度更高等优点.最后,把建立的模型应用于电极控制系统的参数整定上,取得了良好的控制效果,为电极控制提供了理论指导.     

Stochastic Optimal CPS Relaxed Control Methodology for Interconnected Power Systems Using Q-Learning Method

This paper presents the application and design of a novel stochastic optimal control methodology based on the Q-learning method for solving the automatic generation control (AGC) under the new control performance standards (CPS) for the North American Electric Reliability Council (NERC). The aims of CPS are to relax the control constraint requirements of AGC plant regulation and enhance the frequency dispatch support effect from interconnected control areas. The NERC’s CPS-based AGC problem is a dynamic stochastic decision problem that can be modeled as a reinforcement learning (RL) problem based on the Markov decision process theory. In this paper, the Q-learning method is adopted as the RL core algorithm with CPS values regarded as the rewards from the interconnected power systems; the CPS control and relaxed control objectives are formulated as immediate reward functions by means of a linear weighted aggregative approach. By regulating a closed-loop CPS control rule to maximize the long-term discounted reward in the procedure of online learning, the optimal CPS control strategy can be gradually obtained. This paper also introduces a practical semisupervisory group prelearning method to improve the stability and convergence ability of Q-learning controllers during the prelearning process. Tests on the China Southern Power Grid demonstrate that the proposed control strategy can effectively enhance the robustness and relaxation property of AGC systems while CPS compliances are ensured.     

Multivariable Dynamic Modeling for Molten Iron Quality Using Incremental Random Vector Functional-link Networks

Molten iron temperature as well as Si,P,and S contents is the most essential molten iron quality(MIQ)indices in the blast furnace(BF)ironmaking,which requires strict monitoring during the whole ironmaking production.However,these MIQ parameters are difficult to be directly measured online,and large-time delay exists in offline analysis through laboratory sampling.Focusing on the practical challenge,a data-driven modeling method was presented for the prediction of MIQ using the improved multivariable incremental random vector functional-link networks(M-I-RVFLNs).Compared with the conventional random vector functional-link networks(RVFLNs)and the online sequential RVFLNs,the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems.Moreover,the proposed M-I-RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-output(MIMO)dynamic system,which is suitable for the BF ironmaking process in practice.Ultimately,industrial experiments and contrastive researches have been conducted on the BF No.2in Liuzhou Iron and Steel Group Co.Ltd.of China using the proposed method,and the results demonstrate that the established model produces better estimating accuracy than other MIQ modeling methods.     

确定性多变量自校正控制的稳定性、收敛性和鲁棒性

本文用基于传递函数概念的虚拟等价系统方法统一分析各种类型的多变量确定性自校正控制系统的稳定性、收敛性和鲁棒性,分别针对参数估计收敛到真值、参数估计收敛到非真值以及参数估计不收敛的3种情况给出若干定理、推论和注释.在各个判据的基础上,进一步深化对确定性多变量自校正控制系统的理解.所得结论说明:参数估计的收敛性不是确定性多变量自校正控制系统稳定和收敛的必要条件;系统自身的反馈信息对确定性多变量自校正控制是充分的,即外加激励信号不是必要的.      

矿热炉电极下放微机控制系统

通过分析目前矿热炉电极下放的方式所存在的缺陷，提出了利用单片机来控制矿热炉电极下放的方法，并对控制系统设计进行了介绍。本系统具有在线升级功能。     

用Simadyn-D改造宝钢150t电弧炉整流器控制系统

宝钢炼钢厂150 t电弧炉是我国目前最大的直流电弧炉,2003年对其整流器控制系统进行了改造。新系统采用的硬件是西门子公司的Simadyn-D系统,采用CFC编程环境。由于新系统可以与冶炼弧流模型紧密结合,因此保证了在各冶炼阶段调节器性能的最优。