前馈控制在层流冷却系统中的实现

以热连轧层冷却控制系统为实际背景，分析了其输入输出的关系，得到了一套适用性较强的控制模型及前馈控制算法，在实际工业控制生产中得到较好的应用。     

前馈控制在层流冷却系统中的实现

以热连轧层冷却控制系统为实际背景，分析了其输入输出的关系，得到了一套适用性较强的控制模型及前馈控制算法，在实际工业控制生产中得到较好的应用。     

神经网络结合数学模型预测带钢卷取温度

提出了一种与传统数学模型相结合的神经网络建模方法，用于预测热轧带钢的卷取温度。文中以实际生产数据为实例进行了计算，证明效果良好，有在线实际应用的前景。     

水泥窑窑是密封改造

以5＃窑窑尾密封改造为例，论述了改造前后设备构造及运转机理，并介绍了实际运转效果。     

圆盘剪剪切中出现边部鱼鳞裂问题的探讨

以板边通过上下导板所受阻力为对象，用理论与实际相结合的方法，为解决钢板边部断面鱼鳞裂问题进行了一系列探讨，找出了原因，采取了措施，获得了较好的效果。     

调整无芯工频感应熔炼炉功率因数对系统三相电流对称运行的影响

本文以无芯工频感应熔炼炉（以下简称工频炉）在烘炉期间所采集到的实际数据为依据，从调整工频炉电工功率因数入手，应用电路理论进行了一些分析，文中重点探讨了调整功率因数对三相电流对称运行的影响，并做出了结论，对生产实际具有一定的指导作用。     

牙条拖运机传动系统的技术改进

鞍钢大型厂重轨加工线牙条拖运机传动系统原设计不适应生产需要，经常出现事故，技术改进中以经济效益为目标，以科技进步为前提，从生产实际出发，重新设计解决了问题，取得成效，很有交流价值。     

铜基合金电阻率的测量方法

以测量银铜合金的电阻率为例，从测量原理、测量技术要求等方面进行了分析，说明了测量铜合金电阻率的方法，解决了一些实际测量问题。     

旧线铁路勘测设计有关问题研究

本文以实际工程为例，为工厂铁路改扩建旧线复测要求，测量成果验收与配轨设计三方面进行了较为深入的研究，可作工程设计参考借鉴。     

浅议转炉出钢过程的脱硫

本文通过对脱硫反应的热力学及动力学条件分析，及以实际生产为例，阐述了出钢过程脱硫的可行性及重要性。     

蓄热式加热炉模糊控制系统

主要介绍应用于热轧厂的蓄热式加热炉控制系统的组成和一些特点。探讨研究蓄热式加热炉的一种先进的燃烧控制方法，用模糊算法替代传统的PID算法，用于蓄热式加热炉的温度控制、混合煤气热值补偿和空燃比修正控制。实践结果表明．加热炉模糊控制系统抗干扰能力强，能有效地提高钢坯温度控制精度以及控制系统的稳定性和快速响应．取得良好的控制效果和节能减排作用。     

浅谈模糊控制理论在抛丸机过程控制中的应用

根据模糊控制理论,分析喷丸机的运行过程。通过模糊控制算法达到全自动控制,可以有效地进行自动运行提高生产效率。     

双机架平整机中间张力控制系统的模糊控制

阐述双机架平整机中间张力自动控制系统中的模糊控制，详细地介绍了中间张力模糊控制的原理及其组成、算法流程。生产实践证明，中间张力采用模糊控制响应快、超调小，张力波动在5％以内。     

磨矿分级模糊仿人智能控制系统的研究

针对磨矿分级系统的特点，在仿人控制基础上，利用模糊控制优点，运用新的控制算法，对控制系统进行仿真。结果表明，仿人模糊控制具有很强的抗滞后和抗干扰性等优点。     

模糊自适应遗传算法及其在化工中的应用

Considering that the performance of a genetic algorithm (GA) is affected by many factors and their relationships are complex and hard to be described, a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems. In FAGA, immune theory is used to improve the performance of selection operation. And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences, which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters. The experiments show that FAGA can efficiently overcome shortcomings of GA, I.e., premature and slow, and obtain better results than two typical fuzzy Gas. Finally, FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.     

Cloud Neural Fuzzy PID Hybrid Integrated Algorithm of Flatness Control

In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.     

Intelligent Control of Grate-kiln-cooler Process of Iron Ore Pellets Using a Combination of Expert System Approach and Takagi-Sugeno Fuzzy Model

Grate-kiln-cooler has become a major process of producing iron ore pellets in China. Due to the diversity of the raw materials used and the multi-device multi-variable characteristics,this process still encounters with control problem. An attempt was proposed to deal with this issue. The three-device-integrated feature of the process was firstly analyzed to obtain control strategy,and then an intelligent control system using a combination of expert system approach and Takagi-Sugeno( T-S) fuzzy model was developed. Expert system approach was used to diagnose and remedy the abnormal conditions,while T-S fuzzy model was used to stabilize the thermal state. In the construction of T-S fuzzy rules,antecedents were identified by fuzzy c-mean clustering algorithm incorporated with subtractive clustering algorithm,and consequent parameters were identified by recursive least square algorithm. The control system was applied in a Chinese pelletizing plant and the application results demonstrated its effectiveness of stabilizing the thermal states within three devices.     

A fuzzy adaptive learning control network with on-line structure and parameter learning

This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of input/output variables increase, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the proposed learning algorithm partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the proposed learning algorithm can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. The users thus need not give it any a priori knowledge or even any initial information on these. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning Control Network (RFALCON) is further proposed. The proposed RFALCON is constructed by integrating two FALCONs, one FALCON as a critic network, and the other as an action network. By combining temporal difference techniques, stochastic exploration, and a proposed on-line supervised structure/parameter learning algorithm, a reinforcement structure/parameter learning algorithm is proposed, which can construct a RFALCON dynamically through a reward/penalty signal. The ball and beam balancing system is presented to illustrate the performance and applicability of the proposed models and learning algorithms.     

Roll Subsectional Cooling Adaptive Fuzzy Control Based on Fuzzy Model Inversion

 Flatness is an important equality indicator of strip rolling and roll sub-sectional cooling is an important method for flatness control, especially for high order flatness component control. It is very hard to build the mathematic model of roll sub-sectional cooling because of its characteristics of nonlinearity, hysteresis quality and strong coupling etc. In order to improve the control effect of roll sub-sectional cooling control model, the roll sub-sectional cooling adaptive fuzzy control model based on fuzzy model inversion is built according to the separation principle of fuzzy form on the basis of the conventional fuzzy control model, where the parameters of the fuzzy controller can be dynamically regulated according to the change of rolling conditions. Simulation experiment results of the model indicate that the proposed roll sub-sectional cooling adaptive fuzzy control model based on fuzzy model inversion has high control precision and rapid response speed with strong self-learning and anti-interference capacity and a new method is provided for high-precision flatness control.     

基于模糊推理的板形自动控制算法及其应用

 为了提高带钢板形质量,开发了板形自动控制系统应用于连退平整机上。该控制系统使用了基于模糊推理的板形自动控制算法,算法根据板形偏差的大小及变化趋势在线整定增益系数。当板形偏差过大或变化过快时,该算法能快速将板形调整到目标值,从而能确保板形偏差始终在较小范围内。目前,算法已经应用在宝钢2条连续退火机组上,应用效果良好。