基于PSO的自适应广义预测微燃机控制
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摘要
针对微型燃气轮机运行时的大时滞、非线性和时变性等特点,基于系统辨识理论和自适应广义预测控制算法设计了一种微型燃气轮机的转速控制器。首先,通过现场采集微型燃气轮机转速控制系统输入(燃料量)-输出(控制转速)的数据,利用变遗忘因子递推最小二乘算法(FFRLS)进行离线辨识,辨识出微型燃气轮机转速控制系统在特定负荷工况下的受控自回归积分滑动平均模型(CARIMA)。然后,基于所辨识的CARIMA模型,研究并设计了基于粒子群算法(PSO)的自适应广义预测控制器。最后,在MATLAB仿真软件中进行了仿真。仿真结果表明,当系统负荷发生突变时,燃料量流量响应迅速,控制转速超调量较小,过渡过程时间较短,跟踪效果好,鲁棒性较强,控制性能好。
According to the characteristics of large time delay, nonlinearity and time-varying in micro gas turbines, based on the theory of system identification and adaptive generalized predictive control algorithm, a new method of micro turbine speed control is presented. Frist, through the acquisition of the micro gas turbine rotor system input(fuel)-output(rotor speed) data, the CARIMA model of the micro gas turbine rotor speed system is identified by using the FFRLS algorithm. Then, an adaptive generalized predictive controller of PSO is designed based on the identified CARIMA model. Finally, simulations are carried out in MATLAB.Simulation results show that, when the load is abrupt, the fuel quantity is fast, the speed of the rotating speed is small, the tracking effect is perfect, the robustness is strong, and the control performance is also good.
According to the characteristics of large time delay, nonlinearity and time-varying in micro gas turbines, based on the theory of system identification and adaptive generalized predictive control algorithm, a new method of micro turbine speed control is presented. Frist, through the acquisition of the micro gas turbine rotor system input(fuel)-output(rotor speed) data, the CARIMA model of the micro gas turbine rotor speed system is identified by using the FFRLS algorithm. Then, an adaptive generalized predictive controller of PSO is designed based on the identified CARIMA model. Finally, simulations are carried out in MATLAB.Simulation results show that, when the load is abrupt, the fuel quantity is fast, the speed of the rotating speed is small, the tracking effect is perfect, the robustness is strong, and the control performance is also good.
引文
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[2]ROWEN W I.Simplified mathematical representations single shaft ga turbines in mechanical drive service[J].Turbo machinery Int,1992(8)26-32.
[3]赵景峰,叶春,秦春申.燃气轮机及其控制系统的综合建模研究[J].华东电力,2005,33(4):13-16.Zhao J F,Ye C,Qin Chunshen.Study on integrate modeling of gas turbine and its control system[J].East China Electric power,200533(4):13-16.
[4]王晓暄,张春友,李爱平.微型燃气轮机转速控制系统的设计[J].控制工程,2005,12(1):73-76.Wang X X,Zhang C Y,Li A P.Design of microturbine rotate speed control system.Control Engineering of China,2005,12(1):73-76.
[5]席裕庚,李德伟,林姝.模型预测控制-现状与挑战[J].自动化学报,2013,39(3):222-236.Xi Y G,Li D W,Ling Z.Model predicitive control-status and challenges[J].Acta Automatica Sinica,2013,39(3):222-236.
[6]庞中华,崔红.系统辨识与自适应控制MATLAB仿真[M].北京航空航天大学出版社,2009.Pang Z H,Cui H.MATLAB simulation of system identification and adaptive control[M].Beijing University press,2009.
[7]魏静.燃气轮机控制技术研究[D].南京:东南大学,2008.Wei J.Study on technology of gas turbine[D].NanJing:Southeas University,2008.
[8]邹翔,崔宝侠,顾鹏.改进的广义预测控制算法在热力站控制中的应用[J].电子设计工程,2013,21(5):94-96.Zou X,Cui B X,Gu P.Improvement of the generalized predictiv control algorithm in thermal station control application[J].Electroni Design Engineering,2013,21(5):94-96.
[9]蒋朝辉,李学明,桂卫华.大时滞系统全参数自适应控制策略[J]中南大学学报,2012,43(1):195-201.Jiang Z H,Li X M,Gui W H.All parameters adaptive predictiv control strategy for long time-delay system[J].Journal of Centra South University,2012,43(1):195-201.
[10]卢丹丹,袁枫林,丁帅,等.间歇反应器自适应广义预测控制研究[J].控制工程,2015,22(5):891-895.Lu D D,Yuan F L,Ding S,et al.Research of adaptive generalized predicitive control of batch reactor[J].Control Engineering o China,2015,22(5):891-895.
[11]郭伟,陈琛,王汉杰,等.一种改进的多变量广义预测控制在球磨机制粉系统中的应用[J].控制工程,2016,23(1):48-53.Guo W,Chen C,Wang H J,et al.Applications of an improved multivariable generalized predictive control in ball mill pulverizing system[J].Control Engineering of China,2016,23(1):48-53.
[12]Guzman J L,Berenguel M,Dormido S.Interactive teaching o constrained generalized predictive control[J].Control Systems IEEE,2005,25(2):52-56.
[13]杨智,陈颖.改进粒子群算法及其在PID整定中的应用[J].控制工程,2016,23(2):161-166.Yang Z,Chen Y.Improved particle swarm optimization and it application in PID tuning[J].Control Engineering of China,201623(2):161-166.
[14]吴岳,李国勇.基于改进PSO算法的广义预测控制[J].计算机应用与软件,2014,31(7):272-274.Wu Y,Li G Y.Generalised predictive control based on improved particle swarm optimization[J].Computer Application and Software2014,31(7):272-274.
[15]Huang C,pan H.Reactor temperature prediction control algorithm research based on the PSO[C].Control Conference,IEEE,2013.
[16]罗犇,邵之江,徐祖华,等.一种新的基于PSO的MPC及其硬件实现[J].控制工程,2013,20(3):227-230.Luo B,Shao Z J,Xu Z H,et al.PSO based model predictive contro and its hardware implementation[J].Control Engineering of China2013,20(3):227-230.