基于多变量PID神经网络的双进双出磨煤机控制研究
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摘要
双进双出磨煤机广泛应用于国内外火力发电机组,是制粉系统的主体设备,其安全、经济运行直接影响到电厂的生产能力、能耗等各项技术经济指标。因此,研究如何实现磨煤机的自动控制和优化运行,对降低制粉单耗、节约能源具有重要的实际意义。
双进双出磨煤机制粉系统是一个具有非线性、慢时变、大延迟等特点的多变量耦合系统,各被控量与控制量之间耦合严重,动态特性复杂,很难建立精确的数学模型,这使得常规控制方法很难获得满意的控制效果。本文从智能控制的角度对双进双出磨煤机的控制进行了深入研究,主要内容如下：
(1)介绍了双进双出磨煤机的总体结构和工作原理、对其动态特性和影响因素进行了详细分析,深入研究了磨煤机的数学模型、控制目的及要求。
(2)研究了多变量PID神经元网络的结构和算法,针对磨煤机制粉系统的特点,设计了双进双出磨煤机PID神经元网络控制系统。
(3)研究了粒子群算法的基本原理,深入了解其改进方法,提出了基于排队思想的改进粒子群算法,给出了算法改进思想和实现步骤,并进行了函数试验分析,结果表明提出的改进粒子群算法比标准粒子群算法具有更好收敛速度和训练精度,能有效避免搜索陷入局部极小值。
(4)利用提出的改进粒子群算法对PID神经元网络控制器初始参数进行离线优化,然后将得到的最优解带入网络并采用误差反传(BP)算法对网络权值进行在线调整,避免网络陷入局部极小值,保证系统不会出现大的超调和震荡。仿真结果表明,经过改进PSO算法优化后的多变量PID神经网络控制器不仅具有较好的稳态性能和动态性能,而且还具有很强的自学习和自适应能力,能较好地解决制粉系统的耦合性、时变性等问题。
The BBD ball mill is one of the key equipments of pulverizing system in power plants, which is widely used in thermal power generating units, its security and economy running state effects the main technologic and economic targets of the process system directly, such as production ability and energy consumption. Thus, the research of the automatic control and the optimal operation is so significant to decrease of the per-consuming and cutting energy cost.
The BBD ball mill is such a multivariable coupling system that it has the characters of nonlinearity, slow time-varying and huge delay. There's a strict coupling between the controlled and controlling variables, a complex character of the dynamic system, it is so difficult for us to build an accurate mathematic model, also, it's hard to obtain a satisfied results through the ordinary controlling methods. Thus, in this paper, we make a deep research of the controlling of the BBD ball mill from the points of the intelligence control. The main content of this paper contains the following:
(1) Make an introduction of the gross structure and working principle of BBD ball mill and make detailed analysis of the dynamic properties and influencing factors. After that, make a deeply research of the mill's mathematical models, control objectives and requirements.
(2) By doing a research of the structure and algorithm based on multivariable PID neural network, I designed a PID neural network control system of BBD ball mill for the property of the milling system.
(3) Make a research of particle swarm optimization (PSO) and obtain a deep understanding of its improving method. Then I raised an improved PSO based on the queue theory, not only gave out the improving idea and implement procedures but also the formula experiment analysis. The result showed that the improved PSO had a quicker convergence speed and a better training accuracy than the standard PSO, so it could effectively keep the search away from falling into the local minimum value.
(4) Utilize the improved PSO to optimize the initial parameters of the PID neural network controller, and then insert the optimal solution to the network to adjust the network weight online combined with using error backward BP algorithm. The simulation results show that, the multivariable PID neural network controller which is improved by the PSO has a better steady and dynamic capabilities, also with a strong self-learning and self-adaptive abilities, so it can solve the problem of the coupling and time-varying for the milling system commendably.
双进双出磨煤机制粉系统是一个具有非线性、慢时变、大延迟等特点的多变量耦合系统,各被控量与控制量之间耦合严重,动态特性复杂,很难建立精确的数学模型,这使得常规控制方法很难获得满意的控制效果。本文从智能控制的角度对双进双出磨煤机的控制进行了深入研究,主要内容如下：
(1)介绍了双进双出磨煤机的总体结构和工作原理、对其动态特性和影响因素进行了详细分析,深入研究了磨煤机的数学模型、控制目的及要求。
(2)研究了多变量PID神经元网络的结构和算法,针对磨煤机制粉系统的特点,设计了双进双出磨煤机PID神经元网络控制系统。
(3)研究了粒子群算法的基本原理,深入了解其改进方法,提出了基于排队思想的改进粒子群算法,给出了算法改进思想和实现步骤,并进行了函数试验分析,结果表明提出的改进粒子群算法比标准粒子群算法具有更好收敛速度和训练精度,能有效避免搜索陷入局部极小值。
(4)利用提出的改进粒子群算法对PID神经元网络控制器初始参数进行离线优化,然后将得到的最优解带入网络并采用误差反传(BP)算法对网络权值进行在线调整,避免网络陷入局部极小值,保证系统不会出现大的超调和震荡。仿真结果表明,经过改进PSO算法优化后的多变量PID神经网络控制器不仅具有较好的稳态性能和动态性能,而且还具有很强的自学习和自适应能力,能较好地解决制粉系统的耦合性、时变性等问题。
The BBD ball mill is one of the key equipments of pulverizing system in power plants, which is widely used in thermal power generating units, its security and economy running state effects the main technologic and economic targets of the process system directly, such as production ability and energy consumption. Thus, the research of the automatic control and the optimal operation is so significant to decrease of the per-consuming and cutting energy cost.
The BBD ball mill is such a multivariable coupling system that it has the characters of nonlinearity, slow time-varying and huge delay. There's a strict coupling between the controlled and controlling variables, a complex character of the dynamic system, it is so difficult for us to build an accurate mathematic model, also, it's hard to obtain a satisfied results through the ordinary controlling methods. Thus, in this paper, we make a deep research of the controlling of the BBD ball mill from the points of the intelligence control. The main content of this paper contains the following:
(1) Make an introduction of the gross structure and working principle of BBD ball mill and make detailed analysis of the dynamic properties and influencing factors. After that, make a deeply research of the mill's mathematical models, control objectives and requirements.
(2) By doing a research of the structure and algorithm based on multivariable PID neural network, I designed a PID neural network control system of BBD ball mill for the property of the milling system.
(3) Make a research of particle swarm optimization (PSO) and obtain a deep understanding of its improving method. Then I raised an improved PSO based on the queue theory, not only gave out the improving idea and implement procedures but also the formula experiment analysis. The result showed that the improved PSO had a quicker convergence speed and a better training accuracy than the standard PSO, so it could effectively keep the search away from falling into the local minimum value.
(4) Utilize the improved PSO to optimize the initial parameters of the PID neural network controller, and then insert the optimal solution to the network to adjust the network weight online combined with using error backward BP algorithm. The simulation results show that, the multivariable PID neural network controller which is improved by the PSO has a better steady and dynamic capabilities, also with a strong self-learning and self-adaptive abilities, so it can solve the problem of the coupling and time-varying for the milling system commendably.
引文
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[2]Alvaro K.Imai, Ramon R.Costa, Liu Hsu, et al. Multivariable adaptive control using high-frequency gain matrix factorization. IEEE Transactions on automatic control,2004,49(7).
[3]Jankowski Robert, Domanski Pawell, D. Swirski, et al. Optimization of a coal mill using an MPC type controller. American Society of Mechanical Engineers, New York, NY 10016-5990, United States,2003,72.
[4]Barnoski Steve, Labbe Donald, Graves Jim, et al. Coal mill and combustion optimization on a once-through supercritical boiler with multivariable predictive control. ISA, Instrumentation, Systems and Automation Society, Research Triangle Park, NC 27709, United States,2006,1.
[5]Ramasamy M, Narayanan S.S, Rao.Ch.D.P. Control of ball mill grinding circuit using model predictive control scheme. Journal of Process Control,2005(4):273-283.
[6]刘长良,梁伟平,董泽.钢球磨煤机制粉系统的递阶模糊控制.动力工程,2002,22(5)：1973-1969.
[7]刘长良,翟永杰,周黎辉,等.一种基于模型预测的参数自调整模糊控制.自动化仪表,2002,23(8)：19-21.
[8]何菊侃.制粉系统智能解耦模糊控制系统设计与仿真：(硕十学位论文).重庆：重庆大学,2005.
[9]赵华邦.球磨机制粉系统分级预测模糊控制的研究与应用：(硕十学位论文).重庆：重庆大学,2004.
[10]于国强.模糊控制及自适应内模控制在电厂中储式制粉系统控制中的应用研究：(硕士学位论文).南京：东南大学,2004.
[11]孙立明,李东海,姜学智.火电站球磨机制粉系统的自抗扰控制.清华大学学报,2003,43(6)：779-781.
[12]Chen Xisong, Li Shihua, Zhai Junyong et al. Expert system based adaptive matrix control for ball mill grinding circuit. Expert System with Applications,2009(1):716-723.
[13]Chen Xisong, Li Qi, Fei Shumin. Constrained model predictive control in ball mill grinding process. Power Technology,2008(8):31-39.
[14]王介生,王伟.球磨机制粉系统参数自整定PID解耦控制器.控制工程,2007,14(02)：135-139.
[15]李刚,王广军.电站锅炉磨煤机模糊-PID复合控制系统仿真.重庆大学学报,2004,27(01)：107-111.
[16]温玄.中储式磨煤机制粉系统控制算法研究：(硕士学位论文).哈尔滨：哈尔滨理工大学,2006.
[17]李晓枫,张菊华,陈东光,等.中储式制粉系统模糊自寻最优控制的研究开发.国家科技成果.
[18]Li Xiaofeng, Zeng Yuxing, Sun Jian. Fuzzy optimization control system and its application in ball mill pulverizing system. IEEE international Conference on Fuzzy Systems,2006(1):615-620.
[19]Zhou Hong, Li Zhibin, Zhou Disha. Fuzzy-neural decoupling control system based on PLC for ball mill of power plant. IEEE 2002 International Conference on Power System TechnologyProceedings,2002(04)：2340-2340.
[20]王东风.多变量智能控制在电厂制粉系统中的应用研究：(博士学位论文).保定：华北电力大学,2001.
[21]王东风,于希宁,宋之平.制粉系统球磨机的动态数学模型及分布式神经网络逆系统控制.中国电力工程学报,2001,22(01)：97-101.
[22]尚雪莲.中储式钢球磨制粉系统控制及应用：(硕士学位论文).保定：华北电力大学,2005.
[23]尚雪莲,王东风,韩璞.基于微粒群优化的球磨机单神经元自适应解耦控制系统.电力科学与工程,2006(03)：30-33.
[24]华烟旻.基于神经网络的中储式球磨机控制的研究：(硕十学位论文).南京：东南大学,2006.
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