【作者】 曹方；

【导师】 王伟；

【作者基本信息】 大连理工大学 ， 控制理论与控制工程， 2010， 博士

【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。更多还原

【Abstract】 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.更多还原