电渣重熔过程智能控制方法的研究
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摘要
电渣重熔过程是一个具有大惯性、多变量耦合、纯滞后和参数时变的非线性被控过程。多种不确定因素使得传统控制方法难以对其进行有效控制。而智能控制为解决这类复杂被控对象的控制问题提供了有效途径。本文以电渣重熔过程作为研究对象,深入开展了电渣重熔过程数学建模与智能控制方法的研究与应用,建立了电渣重熔过程的数学模型,提出了电渣重熔系统多变量解耦控制模型、基于粗糙集-案例推理的电渣重熔过程设定值参数优化方法和电渣重熔过程不同阶段的冶炼电流和电压控制的复合PID自整定控制策略。
论文主要工作如下：
根据电渣重熔过程生产工艺,分析了熔化率和极间距对电渣质量的影响,并根据电渣重熔过程的动态特性得出抽锭速度、自耗电极下降速度、铸锭高度和自耗电极熔化长度的计算方法；并进行了电渣重熔过程的恒电压和恒电流的给定试验,最后得出电渣重熔过程恒渣阻控制条件的数学模型。
依据电渣重熔生产工艺特点、操作经验和历史数据库,将知识发现与自动控制相结合,案例推理技术与电渣重熔过程专家经验相结合,提出了基于粗糙集-案例推理的智能优化设定模型。根据工艺综合生产指标目标值、电渣重熔过程边界条件和运行工况信息,优化设定电渣重熔的电压和电流,并对案例推理过程中的案例检索、案例修正及存储方法进行了研究。
针对电渣重熔过程的多变量耦合特性,以电渣重熔数学模型为依据,提出一种电渣重熔生产过程智能优化控制策略。它由多变量解耦控制器、智能PID控制器和滞后时间参数模糊自整定方法组成,智能PID控制器参数由基于和声搜索的粒子群算法和模糊算法进行整定。针对电渣重熔的不同阶段特性变化,给出了电渣重熔过程控制系统所使用的PID参数自整定复合控制结构,实现了PID参数的在线自调整,进一步完善了PID控制的自适应性能,提高了控制系统的动态品质和精度。仿真研究和工业应用试验表明所建模型和所提控制方法的有效性。
电渣重熔过程控制采用基于西门子Profibus-DP现场总线和工业以太网技术的集散控制系统,实现了电渣重熔生产过程的集中管理和分散控制；最后对上述智能建模和优化控制方法在电渣重熔生产过程中的工业应用实验结果进行了论述,结果表明控制精度由原来常规控制的10%-15%提高到1%-3%。并且,该控制系统响应时间短,跟踪性好,稳定性高,控制效果良好。保证了生产的铸锭品质优良,单位能耗降低。这对提高电渣冶金企业整体经济效益具有非常重要的意义；同时也为复杂工业过程优化控制提供值得借鉴的工业化实现方法。
Electroslag remelting (ESR) process is a complex nonlinear system with inertia, multivariable coupling, time-delay and parameter change. These uncertainty factors make it difficult to perform the control task of total electroslag remelting process effectively by using conventional control methods. The intelligent control theory provides an efficient approach to realize the control of this kind of complex systems. Based on the mechanism and technique of electroslag remelting furnace, an intelligent control strategy is proposed in this dissertation by comprehensive utilization of mathematical model and intelligent control methods. The strategy includes the a multivariable decoupling control model of electroslag remelting systems, an intelligent optimal setting control strategy based on rough set and case-based reasoning (CBR) method, and a self-tuning PID compound control strategy of ESR current and voltage on different phases. The following research has been carried in this dissertation.
The dissertation analyses the influence of the melting rate and the distance between poles on the quality of slag based on technique of ESR process. According to the dynamic characteristics of ESR, a calculation method is educed about the speed of drawing ingot, the rate of decline electrode, height of ingot and length of electrode. Experiments of set-point of ESR voltage and current are carried on and a mathematical model of constant slag resistance process is obtained in the dissertation.
An intelligent optimal setting control strategy based on rough set and CBR method for controlling the ESR process was proposed according to the ESR technics characteristic, operation experiences and history database. The proposed control strategy optimizes ESR voltage and current set-points based on the integrated production process indicators under target, the boundary conditions and operating states of ESR process. The case search, revision and storage method in the CBR process are illustrated in detail.
Aiming at the multivariable coupling characteristics of ESR process, an intelligent control strategy is proposed based on ESR mathematical model. It consists of multivariable decoupling controller, intelligent PID controller and the fuzzy self-tuning of the time delay. The intelligent PID controller parameters are optimized by Harmony Search based Particle Swarm Optimization (HS-PSO) algorithm and the fuzzy algorithm. A complex control structure of PID controller is proposed on different phase of ESR control. It actualizes self-tuning of PID on line, and perfects self-adjusting capability of PID control. The simulation research and industrial application experiments show that the model and the proposed control method are effective.
The ESR production process utilized Siemens Profibus-DP fieldbus and industrial Ethernet technology to constitute the distributed control system in order to realize the centralized management and decentralized control on the ESR process. Finally, the intelligent modeling and control methods are applied to an ESR industrial production process and the application results are discussed. The results show that the control precision is improved from 10% to 15% of conventional control to 1% to 3% of the proposed control strategy. The system response time, tracking performance and stability are improved. The reseatch results are useful in improving the overall economic benefits of ESR enterprises and also provide a useful means for optimal control of complex industrial processes.
论文主要工作如下：
根据电渣重熔过程生产工艺,分析了熔化率和极间距对电渣质量的影响,并根据电渣重熔过程的动态特性得出抽锭速度、自耗电极下降速度、铸锭高度和自耗电极熔化长度的计算方法；并进行了电渣重熔过程的恒电压和恒电流的给定试验,最后得出电渣重熔过程恒渣阻控制条件的数学模型。
依据电渣重熔生产工艺特点、操作经验和历史数据库,将知识发现与自动控制相结合,案例推理技术与电渣重熔过程专家经验相结合,提出了基于粗糙集-案例推理的智能优化设定模型。根据工艺综合生产指标目标值、电渣重熔过程边界条件和运行工况信息,优化设定电渣重熔的电压和电流,并对案例推理过程中的案例检索、案例修正及存储方法进行了研究。
针对电渣重熔过程的多变量耦合特性,以电渣重熔数学模型为依据,提出一种电渣重熔生产过程智能优化控制策略。它由多变量解耦控制器、智能PID控制器和滞后时间参数模糊自整定方法组成,智能PID控制器参数由基于和声搜索的粒子群算法和模糊算法进行整定。针对电渣重熔的不同阶段特性变化,给出了电渣重熔过程控制系统所使用的PID参数自整定复合控制结构,实现了PID参数的在线自调整,进一步完善了PID控制的自适应性能,提高了控制系统的动态品质和精度。仿真研究和工业应用试验表明所建模型和所提控制方法的有效性。
电渣重熔过程控制采用基于西门子Profibus-DP现场总线和工业以太网技术的集散控制系统,实现了电渣重熔生产过程的集中管理和分散控制；最后对上述智能建模和优化控制方法在电渣重熔生产过程中的工业应用实验结果进行了论述,结果表明控制精度由原来常规控制的10%-15%提高到1%-3%。并且,该控制系统响应时间短,跟踪性好,稳定性高,控制效果良好。保证了生产的铸锭品质优良,单位能耗降低。这对提高电渣冶金企业整体经济效益具有非常重要的意义；同时也为复杂工业过程优化控制提供值得借鉴的工业化实现方法。
Electroslag remelting (ESR) process is a complex nonlinear system with inertia, multivariable coupling, time-delay and parameter change. These uncertainty factors make it difficult to perform the control task of total electroslag remelting process effectively by using conventional control methods. The intelligent control theory provides an efficient approach to realize the control of this kind of complex systems. Based on the mechanism and technique of electroslag remelting furnace, an intelligent control strategy is proposed in this dissertation by comprehensive utilization of mathematical model and intelligent control methods. The strategy includes the a multivariable decoupling control model of electroslag remelting systems, an intelligent optimal setting control strategy based on rough set and case-based reasoning (CBR) method, and a self-tuning PID compound control strategy of ESR current and voltage on different phases. The following research has been carried in this dissertation.
The dissertation analyses the influence of the melting rate and the distance between poles on the quality of slag based on technique of ESR process. According to the dynamic characteristics of ESR, a calculation method is educed about the speed of drawing ingot, the rate of decline electrode, height of ingot and length of electrode. Experiments of set-point of ESR voltage and current are carried on and a mathematical model of constant slag resistance process is obtained in the dissertation.
An intelligent optimal setting control strategy based on rough set and CBR method for controlling the ESR process was proposed according to the ESR technics characteristic, operation experiences and history database. The proposed control strategy optimizes ESR voltage and current set-points based on the integrated production process indicators under target, the boundary conditions and operating states of ESR process. The case search, revision and storage method in the CBR process are illustrated in detail.
Aiming at the multivariable coupling characteristics of ESR process, an intelligent control strategy is proposed based on ESR mathematical model. It consists of multivariable decoupling controller, intelligent PID controller and the fuzzy self-tuning of the time delay. The intelligent PID controller parameters are optimized by Harmony Search based Particle Swarm Optimization (HS-PSO) algorithm and the fuzzy algorithm. A complex control structure of PID controller is proposed on different phase of ESR control. It actualizes self-tuning of PID on line, and perfects self-adjusting capability of PID control. The simulation research and industrial application experiments show that the model and the proposed control method are effective.
The ESR production process utilized Siemens Profibus-DP fieldbus and industrial Ethernet technology to constitute the distributed control system in order to realize the centralized management and decentralized control on the ESR process. Finally, the intelligent modeling and control methods are applied to an ESR industrial production process and the application results are discussed. The results show that the control precision is improved from 10% to 15% of conventional control to 1% to 3% of the proposed control strategy. The system response time, tracking performance and stability are improved. The reseatch results are useful in improving the overall economic benefits of ESR enterprises and also provide a useful means for optimal control of complex industrial processes.
引文
[1]常立忠,李正邦.电渣重熔过程中金属凝固的控制方法[J].钢铁,2007,23(04)：56-62.
[2]董学东,隋铁流.国外电渣重熔概况及我国电渣重熔的发展方向[C].中国特殊钢国际学术研讨会.2009.
[3]WEBER V, JARDY A, DUSSOUBS B. A Comprehensive Model of the Electroslag Remelting Process Description and Validation [J]. Metallurgical and Materials Transactions,2009,40(3): 271-280.
[4]PATEL A D. Analytical Model for Electromagnetic Fields in ESR and VAR Processes [J], Proc. of Liquid Metal Proccessing and Casting,2003,205-214.
[5]SARIDIS G N. Anaalytic for Mulation of the Principle of Increasing Precision with Decreasing Intelligence for Intelligent Machines [J].Automatica,1989,25(3):19-34
[6]FU K S, Learning Control Systems and Intelligent Control System[B/OL], [2006-06-20]. http://ieeexplore.ieee.org/Xpllore/guesthome.jsp.
[7]WALTZ M D, FU K S. A Heuristic Approach to Reinforcement Learning Control System[EB/OL], [2006-06-20]. http://ieeexplore.ieee.org/Xpllore/guesthome.jsp.
[8]蔡自兴,贺汉根.智能科学发展的若干问题[J].自动化学报,2002,28(Supp.1)：142-151.
[9]Sehlegel Martin, Stoelanaun Klans, Binder Thomas, et al. Dynamic optimization using adaptive control vector Parameterization [J]. Computers and Chemical Engineering,2005, 29(8):1731-1751.
[10]JAMALUDIN J, RAHIM N A, HEW W P. Development of self tunning fuzzy logic controller for intelligence control of elevator system[J]. Journal of Engineering Applications of Artificial Intelligence,2009,22:1167-1178.
[11]QIN S J, BADGWELL T A. A survey of industrial model Predictive control technology [J]. Control Engineering Praetice,2003,11:733-764.
[12]CHENG W S. An application of adaptive genetic-eural algorithm to sinter's BTP Process [C]. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004,6:3356-3360.
[13]张云生,王彬,王川,杨业.分级递阶控制结构的状态描述和实时实现[J].计算机工程与应用,2002.38(20)：241-243.
[14]王昕,王中杰,杨辉,李少远.多模型自适应控制的分层递阶构造与覆盖性质分析[J].控制理论与应用,2006.23(03)：367-372.
[15]黄黎明,唐朝晖.智能控制过程中模糊专家控制规则的获取[J].计算机工程与应用,2007.43(13)：243-246.
[16]张弘.大滞后系统控制中专家-模糊PID方法的应用[J].计算机工程与应用,2009.45(28)：244-245.
[17]翟廉飞,柴天佑.一类非线性离散系统的神经网络自适应控制[J].东北大学学报(自然科学版),2009,30(11).
[18]王贞艳,张井岗,陈志梅.神经网络滑模变结构控制研究综述[J].信息与控制,2005,34(04)：1002-0411.
[19]田景文,高美娟.人工神经网络算法研究及应用[M].北京：北京理工大学出版社,2006.
[20]阳爱民.模糊分类模型的研究[D].上海：复旦大学.2005.
[21]李杰.带时间延迟的离散模糊系统的鲁棒无源控制研究[D].河北科技大学.2009.
[22]李妍,毛志忠,王福利,王琰.电弧炉电极调节系统的自适应死区补偿控制[J].控制与决策,2010.25(10)：1474-1478.
[23]郭健,陈庆伟,吴益飞,姚斌.一类非线性不确定系统的自适应鲁棒控制[J].控制理论与应用,2010,27(08)：1081-1085.
[24]郭健,姚斌,吴益飞,陈庆伟.具有输入齿隙的一类非线性不确定系统自适应鲁棒控制[J].控制与决策,2010,25(10)：1580-1584.
[25]马戎.智能控制技术在炼钢电弧炉中的应用研究[D].西北工业大学.2006.
[26]杨大勇,李鸣.专家PID控制在过程控制装置上的仿真与应用[J].计算机与现代化,2008,02
[27]李影,徐涛,邢伟.基于进化遗传算法的神经网络优化[J].长春理工大学学报(自然科学版),2006，03：48-50
[28]马戎.智能控制技术在炼钢电弧炉中的应用研究[D].西北工业大学.2006.
[29]李正邦.二十一世纪电渣冶金的展望[J].炼钢,2003,19(02)：1-7.
[30]张莉,王京春,王景标.电渣炉的两种摆动控制原理与分析[J].冶金自动化,2006,9：53-55.
[31]李宛州,贾兴华,王京春.康萨克电渣重熔炉自动控制系统改造[J].钢铁研究学报,2007,19(4)：99-101.
[32]赵丽丽,宋锦春,刘喜海.基于遗传算法的电渣炉重熔过程智能控制研究[J].机械与电子,2008，5：16-19.
[33]王宁,涂健.电渣重熔过程的神经元智能控制[J].自动化学报,1993,19(5)：634-636.
[34]余强,孙国会,姜周华.电渣炉智能控制系统的开发和应用[J].中国冶金,2006.12：20-23.
[35]王宇达,何国青,宋万民.电渣重熔过程智能控制研究与应用[J].工业加热,2005,36(06)：42-45.
[36]赵丽丽,宋锦春,柳洪义.电渣重熔熔速控制过程综合分析[J].冶金设备,2007,5：20-23.
[37]任伟,郑险峰,姜立新等.电渣炉电极调节系统的模糊自适应PID控制[J].冶金自动化,2006,1：15-18.
[38]耿茂鹏,孙达昕.电渣溶铸过程控制与模拟仿真[M].北京：冶金工业出版社,2008.
[39]杨洪.以熔化率和极间距为中心的电渣炉计算机控制模型[C].中国金属学会特殊钢分会.全国电渣冶金年会论文集.北京：2005.57-60.
[40]I.Watson, F. Marir. Case-based reasoning:a review[J]. Knowledge Engineering Review, 1994,9(4):355-381.
[41]谭明皓,柴天佑.基于案例推理的层流冷却过程建模[J].控制理论与应用,2005,22(2)：248-253.
[42]耿增显,柴天佑.基于案例推理的浮选过程智能优化设定[J].东北大学学报(自然科学版),2008,29(6)：761-764.
[43]周平,岳恒,赵大勇等.基于案例推理的软测量方法及在磨矿过程中的应用[J].控制与决策,2006,21(6)：646-650.
[44]龚锦红,基于案例推理的稀土萃取分离过程优化设定控制方法研究[D].华东交通大学,2007.
[45]方明,李天太等.基于实例的不确定检索模型的研究[J].控制与决策,1999,14(1)：93-96.
[46]钟诗胜,王知行.基于模糊相似优先的实例检索模[J].计算机研究与发展,1998,39(5)：810-813.
[47]ZHANG J, MORRIS A J. FuzZy neural networks for nonlinear systems modeling. IEE Proc. Control Theory,1995[C],142(6):551-561.
[48]方明,李天太等.基于实例的模糊相似性度量模型研究[J].计算机工程与设计,1999,20(6)：7-9.
[49]梁云.基于实例的型架设计技术研究[D].西安：西北工业大学.2003.
[50]李锋,冯珊.基于人工神经网络的案例检索与案例维护[J].系统工程与电子技术,2004,26(8)：1053-1056.
[51]PAWLAK Z. Rough sets[J], Intenational Joumal of ComPuter and Information Seience, 1982,11:341-356.
[52]刘清Rough集及Rough推理[M].北京：科学出版社,2003.
[53]曾黄麟.智能计算[M].重庆：重庆大学出版社,2004.
[54]张雪梅.基于rough集理论的属性简约研究[J].计算机仿真,2004,21(10)：66-68.
[55]常犁云,王国撤等一种基于Rough Set理论的属性约简及规则提取方法.软件学报,1999.10(11)：1206-1211.
[56]张振飞.基于案例推理的滚动抽承故障诊断[D].中南大学,2008.
[57]PAWLAK Z. Rough set-theoretical aspects of reasoning about data[M]. Dordrecht:kluwer academic publishers,1991.
[58]SHUSAKU T, HIROSHI T. Extraction of domain knowledge from databases based on rough set theory[C]. IEEE Proceedings of Fifth International Conference on Fuzzy Systems, New Orleans,1996:748-754.
[59]王珏,苗夺谦.关于Rouge Set理论与应用的综述[J].模式识别与人工智能.1996,9(4)：337-344.
[60]韩祯祥,张琦,文福栓.粗糙集理论及其应用综述[J].控制理论与应用.1999,16(2)：153-157.
[61]王介生.球团烧结过程智能控制方法及其应用研究[D],大连：大连理工大学,2009.
[62]PAWLAK Z. Rough Set Theory and Its Applications to Data Analysis[J]. Cybernetics and Systems,1998,29(7):661-688.
[63]王介生,战红仁,王伟.基于粗糙集的T-S模糊神经网络在回转窑烧结过程中的应用[J].华东理工大学学报,2006,32(7)：796-800.
[64]BEZDEK J C. Pattern recognition with fuzzy objective function algorithms [M]. New York:Plenum Press,1982.
[65]XIE X L, BENI G. A validity measure for fuzzy clustering [J].IEEE Trans. On Pattern Analysis and Machine Intelligence,1991,13(8):841-847.
[66]OHRN. A ROSETTA SOFTWARE SYSTEM[EB/OL]. http://Rosetta. lcb. uu. se/.
[67]王雁,王军,王瑞祥.采用对角矩阵解耦法提高变风量空调的性能[J].建筑电气.2004,23(6)：3-7.
[68]王伟,张晶涛,柴天佑.PID参数先进整定方法综述.自动化学报.2000,26(3)：347-355.
[69]段军,机车柴油机数字式电子调速系统智能PID控制理论和技术的研究[D].大连：大连理工大学,2000.
[70]REYNOLDS C W. locksF, Herds and Schools:A Distributed Behavioral Model[J]. Computer Graphics,1987,21(4):25-34.
[71]HEPPNER F,GRENANDER U. A stochastic Nonlinear Model for Coordinated Bird Flocks[M].The Ubiquity of Chaos, AAS Publications,1990:87-92.
[72]KENNEDY J, EBERHART R. Particle swarm optimization. Proc IEEE Int. Conf. on Neural Networks [C]. Perth,1995:1942-1948.
[73]王介生,王金城,王伟.基于粒子群算法的PID控制器参数自整定[J].控制与决策,2005,20(1)：73-76.
[74]段海滨,王道波,黄向华等.基于蚁群算法的PID参数优化[J].武汉大学学报(工学版),2004,37(5)：97-100.
[75]WANG P, KWOK D P. Auto-tuning of classical PID controllers using an advanced genetic algorithm. Proc IEEE Int. Conf. on Power Electronics and Motion Control [C]. SanDiego, 1992:1224-1229.
[76]熊伟丽,徐保国,周其明.基于改进粒子群算法的PID参数优化方法研究[J].计算机工程,2005,31(24)：41-43.
[77]任子武,伞冶,陈俊风.改进PSO算法及在PID参数整定中应用研究[J].系统仿真学报,2006,18(10)：2870-2873.
[78]王博,胡成玉,王永骥.基于Gas/PSO组合算法的水轮机调速系统PID参数寻优[J].华东理工大学学报,2006,32(7)：893-896.
[79]GEEM Z W, KIN J H, LOGANATHAN G V. A New Heuristic Optimization Algorithm:Harmony Search[J]. Simulation,2001,76(2):60-68.
[80]GEEM Z W, JUSTIN C.Williams. Harmony search and ecological optimization[J]. International Journal of Energy and Environment,2007,2(1):150-154.
[81]毛志忠,李健.具有前馈环节的电弧炉电极升降自适应控制器[J].东北大学学报,1996，01：65-68.
[82]刘红波,李少远,柴天佑.一种设计模糊PID复合控制器的新方法及其在电厂控制中的应用[J].动力工程,2004,24(1)：78-82.
[2]董学东,隋铁流.国外电渣重熔概况及我国电渣重熔的发展方向[C].中国特殊钢国际学术研讨会.2009.
[3]WEBER V, JARDY A, DUSSOUBS B. A Comprehensive Model of the Electroslag Remelting Process Description and Validation [J]. Metallurgical and Materials Transactions,2009,40(3): 271-280.
[4]PATEL A D. Analytical Model for Electromagnetic Fields in ESR and VAR Processes [J], Proc. of Liquid Metal Proccessing and Casting,2003,205-214.
[5]SARIDIS G N. Anaalytic for Mulation of the Principle of Increasing Precision with Decreasing Intelligence for Intelligent Machines [J].Automatica,1989,25(3):19-34
[6]FU K S, Learning Control Systems and Intelligent Control System[B/OL], [2006-06-20]. http://ieeexplore.ieee.org/Xpllore/guesthome.jsp.
[7]WALTZ M D, FU K S. A Heuristic Approach to Reinforcement Learning Control System[EB/OL], [2006-06-20]. http://ieeexplore.ieee.org/Xpllore/guesthome.jsp.
[8]蔡自兴,贺汉根.智能科学发展的若干问题[J].自动化学报,2002,28(Supp.1)：142-151.
[9]Sehlegel Martin, Stoelanaun Klans, Binder Thomas, et al. Dynamic optimization using adaptive control vector Parameterization [J]. Computers and Chemical Engineering,2005, 29(8):1731-1751.
[10]JAMALUDIN J, RAHIM N A, HEW W P. Development of self tunning fuzzy logic controller for intelligence control of elevator system[J]. Journal of Engineering Applications of Artificial Intelligence,2009,22:1167-1178.
[11]QIN S J, BADGWELL T A. A survey of industrial model Predictive control technology [J]. Control Engineering Praetice,2003,11:733-764.
[12]CHENG W S. An application of adaptive genetic-eural algorithm to sinter's BTP Process [C]. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004,6:3356-3360.
[13]张云生,王彬,王川,杨业.分级递阶控制结构的状态描述和实时实现[J].计算机工程与应用,2002.38(20)：241-243.
[14]王昕,王中杰,杨辉,李少远.多模型自适应控制的分层递阶构造与覆盖性质分析[J].控制理论与应用,2006.23(03)：367-372.
[15]黄黎明,唐朝晖.智能控制过程中模糊专家控制规则的获取[J].计算机工程与应用,2007.43(13)：243-246.
[16]张弘.大滞后系统控制中专家-模糊PID方法的应用[J].计算机工程与应用,2009.45(28)：244-245.
[17]翟廉飞,柴天佑.一类非线性离散系统的神经网络自适应控制[J].东北大学学报(自然科学版),2009,30(11).
[18]王贞艳,张井岗,陈志梅.神经网络滑模变结构控制研究综述[J].信息与控制,2005,34(04)：1002-0411.
[19]田景文,高美娟.人工神经网络算法研究及应用[M].北京：北京理工大学出版社,2006.
[20]阳爱民.模糊分类模型的研究[D].上海：复旦大学.2005.
[21]李杰.带时间延迟的离散模糊系统的鲁棒无源控制研究[D].河北科技大学.2009.
[22]李妍,毛志忠,王福利,王琰.电弧炉电极调节系统的自适应死区补偿控制[J].控制与决策,2010.25(10)：1474-1478.
[23]郭健,陈庆伟,吴益飞,姚斌.一类非线性不确定系统的自适应鲁棒控制[J].控制理论与应用,2010,27(08)：1081-1085.
[24]郭健,姚斌,吴益飞,陈庆伟.具有输入齿隙的一类非线性不确定系统自适应鲁棒控制[J].控制与决策,2010,25(10)：1580-1584.
[25]马戎.智能控制技术在炼钢电弧炉中的应用研究[D].西北工业大学.2006.
[26]杨大勇,李鸣.专家PID控制在过程控制装置上的仿真与应用[J].计算机与现代化,2008,02
[27]李影,徐涛,邢伟.基于进化遗传算法的神经网络优化[J].长春理工大学学报(自然科学版),2006，03：48-50
[28]马戎.智能控制技术在炼钢电弧炉中的应用研究[D].西北工业大学.2006.
[29]李正邦.二十一世纪电渣冶金的展望[J].炼钢,2003,19(02)：1-7.
[30]张莉,王京春,王景标.电渣炉的两种摆动控制原理与分析[J].冶金自动化,2006,9：53-55.
[31]李宛州,贾兴华,王京春.康萨克电渣重熔炉自动控制系统改造[J].钢铁研究学报,2007,19(4)：99-101.
[32]赵丽丽,宋锦春,刘喜海.基于遗传算法的电渣炉重熔过程智能控制研究[J].机械与电子,2008，5：16-19.
[33]王宁,涂健.电渣重熔过程的神经元智能控制[J].自动化学报,1993,19(5)：634-636.
[34]余强,孙国会,姜周华.电渣炉智能控制系统的开发和应用[J].中国冶金,2006.12：20-23.
[35]王宇达,何国青,宋万民.电渣重熔过程智能控制研究与应用[J].工业加热,2005,36(06)：42-45.
[36]赵丽丽,宋锦春,柳洪义.电渣重熔熔速控制过程综合分析[J].冶金设备,2007,5：20-23.
[37]任伟,郑险峰,姜立新等.电渣炉电极调节系统的模糊自适应PID控制[J].冶金自动化,2006,1：15-18.
[38]耿茂鹏,孙达昕.电渣溶铸过程控制与模拟仿真[M].北京：冶金工业出版社,2008.
[39]杨洪.以熔化率和极间距为中心的电渣炉计算机控制模型[C].中国金属学会特殊钢分会.全国电渣冶金年会论文集.北京：2005.57-60.
[40]I.Watson, F. Marir. Case-based reasoning:a review[J]. Knowledge Engineering Review, 1994,9(4):355-381.
[41]谭明皓,柴天佑.基于案例推理的层流冷却过程建模[J].控制理论与应用,2005,22(2)：248-253.
[42]耿增显,柴天佑.基于案例推理的浮选过程智能优化设定[J].东北大学学报(自然科学版),2008,29(6)：761-764.
[43]周平,岳恒,赵大勇等.基于案例推理的软测量方法及在磨矿过程中的应用[J].控制与决策,2006,21(6)：646-650.
[44]龚锦红,基于案例推理的稀土萃取分离过程优化设定控制方法研究[D].华东交通大学,2007.
[45]方明,李天太等.基于实例的不确定检索模型的研究[J].控制与决策,1999,14(1)：93-96.
[46]钟诗胜,王知行.基于模糊相似优先的实例检索模[J].计算机研究与发展,1998,39(5)：810-813.
[47]ZHANG J, MORRIS A J. FuzZy neural networks for nonlinear systems modeling. IEE Proc. Control Theory,1995[C],142(6):551-561.
[48]方明,李天太等.基于实例的模糊相似性度量模型研究[J].计算机工程与设计,1999,20(6)：7-9.
[49]梁云.基于实例的型架设计技术研究[D].西安：西北工业大学.2003.
[50]李锋,冯珊.基于人工神经网络的案例检索与案例维护[J].系统工程与电子技术,2004,26(8)：1053-1056.
[51]PAWLAK Z. Rough sets[J], Intenational Joumal of ComPuter and Information Seience, 1982,11:341-356.
[52]刘清Rough集及Rough推理[M].北京：科学出版社,2003.
[53]曾黄麟.智能计算[M].重庆：重庆大学出版社,2004.
[54]张雪梅.基于rough集理论的属性简约研究[J].计算机仿真,2004,21(10)：66-68.
[55]常犁云,王国撤等一种基于Rough Set理论的属性约简及规则提取方法.软件学报,1999.10(11)：1206-1211.
[56]张振飞.基于案例推理的滚动抽承故障诊断[D].中南大学,2008.
[57]PAWLAK Z. Rough set-theoretical aspects of reasoning about data[M]. Dordrecht:kluwer academic publishers,1991.
[58]SHUSAKU T, HIROSHI T. Extraction of domain knowledge from databases based on rough set theory[C]. IEEE Proceedings of Fifth International Conference on Fuzzy Systems, New Orleans,1996:748-754.
[59]王珏,苗夺谦.关于Rouge Set理论与应用的综述[J].模式识别与人工智能.1996,9(4)：337-344.
[60]韩祯祥,张琦,文福栓.粗糙集理论及其应用综述[J].控制理论与应用.1999,16(2)：153-157.
[61]王介生.球团烧结过程智能控制方法及其应用研究[D],大连：大连理工大学,2009.
[62]PAWLAK Z. Rough Set Theory and Its Applications to Data Analysis[J]. Cybernetics and Systems,1998,29(7):661-688.
[63]王介生,战红仁,王伟.基于粗糙集的T-S模糊神经网络在回转窑烧结过程中的应用[J].华东理工大学学报,2006,32(7)：796-800.
[64]BEZDEK J C. Pattern recognition with fuzzy objective function algorithms [M]. New York:Plenum Press,1982.
[65]XIE X L, BENI G. A validity measure for fuzzy clustering [J].IEEE Trans. On Pattern Analysis and Machine Intelligence,1991,13(8):841-847.
[66]OHRN. A ROSETTA SOFTWARE SYSTEM[EB/OL]. http://Rosetta. lcb. uu. se/.
[67]王雁,王军,王瑞祥.采用对角矩阵解耦法提高变风量空调的性能[J].建筑电气.2004,23(6)：3-7.
[68]王伟,张晶涛,柴天佑.PID参数先进整定方法综述.自动化学报.2000,26(3)：347-355.
[69]段军,机车柴油机数字式电子调速系统智能PID控制理论和技术的研究[D].大连：大连理工大学,2000.
[70]REYNOLDS C W. locksF, Herds and Schools:A Distributed Behavioral Model[J]. Computer Graphics,1987,21(4):25-34.
[71]HEPPNER F,GRENANDER U. A stochastic Nonlinear Model for Coordinated Bird Flocks[M].The Ubiquity of Chaos, AAS Publications,1990:87-92.
[72]KENNEDY J, EBERHART R. Particle swarm optimization. Proc IEEE Int. Conf. on Neural Networks [C]. Perth,1995:1942-1948.
[73]王介生,王金城,王伟.基于粒子群算法的PID控制器参数自整定[J].控制与决策,2005,20(1)：73-76.
[74]段海滨,王道波,黄向华等.基于蚁群算法的PID参数优化[J].武汉大学学报(工学版),2004,37(5)：97-100.
[75]WANG P, KWOK D P. Auto-tuning of classical PID controllers using an advanced genetic algorithm. Proc IEEE Int. Conf. on Power Electronics and Motion Control [C]. SanDiego, 1992:1224-1229.
[76]熊伟丽,徐保国,周其明.基于改进粒子群算法的PID参数优化方法研究[J].计算机工程,2005,31(24)：41-43.
[77]任子武,伞冶,陈俊风.改进PSO算法及在PID参数整定中应用研究[J].系统仿真学报,2006,18(10)：2870-2873.
[78]王博,胡成玉,王永骥.基于Gas/PSO组合算法的水轮机调速系统PID参数寻优[J].华东理工大学学报,2006,32(7)：893-896.
[79]GEEM Z W, KIN J H, LOGANATHAN G V. A New Heuristic Optimization Algorithm:Harmony Search[J]. Simulation,2001,76(2):60-68.
[80]GEEM Z W, JUSTIN C.Williams. Harmony search and ecological optimization[J]. International Journal of Energy and Environment,2007,2(1):150-154.
[81]毛志忠,李健.具有前馈环节的电弧炉电极升降自适应控制器[J].东北大学学报,1996，01：65-68.
[82]刘红波,李少远,柴天佑.一种设计模糊PID复合控制器的新方法及其在电厂控制中的应用[J].动力工程,2004,24(1)：78-82.
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