动态矩阵控制算法的仿真研究及PLC应用
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摘要
针对具有大迟延、大惯性、时变和非线性等特性的主汽温系统,传统PID已无法满足工业生产的需要。采用动态矩阵控制(DMC)算法,对主蒸汽温度系统进行控制仿真,并利用西门子S7-300 PLC的S7-SCL语言,将DMC算法封装成可供用户调用的功能块,完成了DMC算法的PLC实现。为了增强主蒸汽温度控制系统的抗干扰性,将DMC算法与PID控制策略相结合,设计了DMC-PID串级控制结构,以充分发挥DMC对大延迟对象适应能力强和PID抗干扰能力强的优点。针对主汽温系统时变和非线性的特性,在DMC算法中添加了模型切换策略,完成了对主汽温系统多模型控制的仿真,实现了不同模型之间平滑切换。引入性能指标,通过粒子群算法对DMC控制参数进行优化,结合国电智深EDPF-NT DCS系统,对荥阳电厂主汽温系统进行控制试验。试验结果表明,DMC算法在主汽温控制系统中具有着良好的控制效果,提高了系统动静态性能指标。
For the main steam temperature system which features large delay,large inertia,time varying and nonlinear characteristics,the traditional PID control is unable to meet the demand for the industrial production. The dynamic matrix control( DMC) algorithm is adopted to implement the simulation of control. By using SIEMENS S7-SCL language of S7-300 PLC,the DMC algorithm is encapsulated into function block( FB) function blocks which users can call,and the PLC implementation of DMC algorithm is completed. In order to enhance the anti-interference performance,a DMC-PID cascade control structure is designed by combining the DMC algorithm with the PID control strategy,thus advantages of both DMC and PID are fully played,i. e.,the adaptability to large delay objects and the strong anti-interference capability. In view of the time-varying and nonlinear characteristics of main steam temperature system,the model switching strategy is added to the DMC algorithm,the simulation of multi-model control is completed,and the smooth switching between different models is realized. Particle swarm optimization algorithm is used to optimize the DMC control parameters. With the Guodian Zhishen EDPF-NT DCS as the object,control experiment is carried out for main steam temperature system of Xingyang power plant. The results show that the DMC algorithm has good control effect for the main steam temperature control system,and improves the dynamic and static performance indexes of the system.
For the main steam temperature system which features large delay,large inertia,time varying and nonlinear characteristics,the traditional PID control is unable to meet the demand for the industrial production. The dynamic matrix control( DMC) algorithm is adopted to implement the simulation of control. By using SIEMENS S7-SCL language of S7-300 PLC,the DMC algorithm is encapsulated into function block( FB) function blocks which users can call,and the PLC implementation of DMC algorithm is completed. In order to enhance the anti-interference performance,a DMC-PID cascade control structure is designed by combining the DMC algorithm with the PID control strategy,thus advantages of both DMC and PID are fully played,i. e.,the adaptability to large delay objects and the strong anti-interference capability. In view of the time-varying and nonlinear characteristics of main steam temperature system,the model switching strategy is added to the DMC algorithm,the simulation of multi-model control is completed,and the smooth switching between different models is realized. Particle swarm optimization algorithm is used to optimize the DMC control parameters. With the Guodian Zhishen EDPF-NT DCS as the object,control experiment is carried out for main steam temperature system of Xingyang power plant. The results show that the DMC algorithm has good control effect for the main steam temperature control system,and improves the dynamic and static performance indexes of the system.
引文
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[3]张嘉英,席东民,胡琳静.广义预测控制在锅炉燃烧控制系统中的应用[J].热力发电,2011,40(2):56-59.
[4]赵军.采用专家控制算法提高火电机组自动控制品质[J].热力发电,2007,36(10):52-56.
[5]席裕庚,李德伟.预测控制定性综合理论的基本思路和研究现状[J].自动化学报,2008,34(10):1225-1234.
[6]高山.300 MW机组主汽温系统预测控制方案研究[D].北京:华北电力大学,2015.
[7]安爱民,杨国强,张浩琛,等.基于T-S模糊决策的PMSG风电系统广义预测控制[J].自动化仪表,2016,37(9):1-5.
[8]潘岩,潘维加.动态矩阵预测控制在火电厂中的应用与展望[J].自动化仪表,2014,35(11):17-19.
[9]郭伟,夏友亮,周丽,等.多变量动态矩阵控制算法研究[J].计算机仿真,2014,31(8):360-363.
[10]王进华.二次型最优控制问题中的权矩阵与最优控制律[J].控制与决策,2007,22(8):943-945.
[11]张文安,徐建明,俞立.PID-DMC算法及其在液位控制系统中的应用[J].控制工程,2005,12(1):22-24.
[12]叶向前,崔春雷,易凤飞,等.基于改进型动态矩阵预测的主蒸汽温度串级控制策略研究[J].热力发电,2013,42(7):50-55.
[13]武彬,张栾英.模糊自整定PID控制在主汽温控制中的应用[J].计算机仿真,2015,32(2):387-390.
[14]韩忠旭,阎翠会,张智.锅炉蒸汽温度鲁棒控制系统研究及其稳定性分析[J].中国电机工程学报,2010,30(8):101-109.
[15]李少远.工业过程系统的预测控制[J].控制工程,2010,17(4):407-415.