基于子空间模型的煮糖结晶过程入料流量自适应控制
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摘要
为研究具有非线性大时滞特点的煮糖过程入料流量的自适应控制问题,从系统辨识的角度入手,通过子空间辨识法建立入料流量与电动阀门开度之间的预测模型,利用改进型最小二乘法构建目标函数简化运算,通过滚动优化来整定模型的输出误差以实现对入料流量的自适应控制。仿真结果表明,子空间预测模型的训练时间为0. 888 3 s,平均绝对误差为0. 002 6,明显优于BP神经网络预测模型和RBF神经网络预测模型的预测性能,符合煮糖过程工艺控制的要求。最后通过自主研发的煮糖过程综合实验平台对该控制算法的有效性和优越性进行了实验验证。
In order to study the self-adaptive control of the feed syrup flow during the sugar crystallization process with nonlinear and large time delay,based on the perspective of system identification,the prediction model between the inlet syrup flow and the electric valve opening was established by the subspace identification method,and the improved least square method was used to construct the target. The function simplifies the calculation and adjusts the output error of the model through rolling optimization to achieve adaptive control of the incoming flow. The simulation analysis results show that the training time of the subspace prediction model is 0. 888 3 s,and the average absolute error is 0. 002 6,which is obviously better than those of BP neural network prediction model and RBF neural network prediction,and is in line with the process control requirements of thesugar crystallization process. Finally,the experiment was carried out through the self-developed comprehensive experiment platform for crystallization process which demonstrate the effectiveness and superiority of the control algorithm.
In order to study the self-adaptive control of the feed syrup flow during the sugar crystallization process with nonlinear and large time delay,based on the perspective of system identification,the prediction model between the inlet syrup flow and the electric valve opening was established by the subspace identification method,and the improved least square method was used to construct the target. The function simplifies the calculation and adjusts the output error of the model through rolling optimization to achieve adaptive control of the incoming flow. The simulation analysis results show that the training time of the subspace prediction model is 0. 888 3 s,and the average absolute error is 0. 002 6,which is obviously better than those of BP neural network prediction model and RBF neural network prediction,and is in line with the process control requirements of thesugar crystallization process. Finally,the experiment was carried out through the self-developed comprehensive experiment platform for crystallization process which demonstrate the effectiveness and superiority of the control algorithm.
引文
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[2]MICHAL J,KMINEK M,KMINEK P.Expert control of vacuum pan crystallization[J].IEEE Control Systems,1994,14(5):28-34.
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[5]ROZSA L.On-line monitoring of supersaturation in sugar crystallisation[J].International Sugar Journal,1996,98(1176):660-1.
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[8]HUANG S,TAN K K,LEE T H,et al.Adaptive control of mechanical systems using neural networks[J].IEEE Transactions on Systems,Man and Cybernetics Part C(Applications and Reviews),2007,37(5):897-903.
[9]席裕庚.动态不确定环境下广义控制问题的预测控制[J].控制理论与应用,2000,17(5):665-670.
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[11]MORARI M,LEE J H.Model predictive control:past,present and future[J].Computers&Chemical Engineering,1999,23(4-5):667-682.
[12]DONG J,VERHAEGEN M,HOLWEG E.Closed-loop subspace predictive control for fault tolerant MPC design[J].IFACProceedings Volumes,2008,41(2):3216-3221.
[13]HALLOUZI R,VERHAEGEN M.Fault-tolerant subspace predictive control applied to a Boeing 747 model[J].Journal of Guidance,Control,and Dynamics,2008,31(4):873-883.
[14]VAN OVERSCHEE P,DE MOOR B L.Subspace identification for linear systems:Theory-Implementation-Applications[J].Springer Science&Business Media,2012,23(4):236-252.
[15]LI W,HAN Z,SHAH S L.Subspace identification for FDI in systems with non-uniformly sampled multirate data[J].Automatica,2006,42(4):619-627.
[16]杨华,李少远.一种完全数据驱动的子空间辨识与鲁棒预测控制器设计[J].控制理论与应用,2007,24(5):732-736.
[17]KADALI R,HUANG B,ROSSITER A.A data driven subspace approach to predictive controller design[J].Control Engineering Practice,2003,11(3):261-278.
[18]LAZAR M,HEEMELS W,WWILAND S,et al.Stabilizing model predictive control of hybrid systems[J].IEEE Transactions on Automatic Control,2006,51(11):1813-1818.
[19]YANG H,LI S.Subspace-based adaptive predictive control for a class of nonlinear systems[J].International Journal of Innovative Computing,Information and Control,2005,1(4):743-753.
[2]MICHAL J,KMINEK M,KMINEK P.Expert control of vacuum pan crystallization[J].IEEE Control Systems,1994,14(5):28-34.
[3]ZHANG H,LAKERVELD R,HEIDER P L,et al.Application of continuous crystallization in an integrated continuous pharmaceutical pilot plant[J].Crystal Growth&Design,2014,14(5):2148-2157.
[4]WILSON D I,LEE P L,WHITE E T,et al.Advanced control of a sugar crystallizer[J].Journal of Process Control,1991,1(4):197-206.
[5]ROZSA L.On-line monitoring of supersaturation in sugar crystallisation[J].International Sugar Journal,1996,98(1176):660-1.
[6]樊春丽,朱名日,王玲妹.BP神经网络在煮糖结晶过程建模的应用[J].计算机仿真,2009,26(5):100-102.
[7]DESAI K,BADHE Y,TAMBE S S,et al.Soft-sensor development for fed-batch bioreactors using support vector regression[J].Biochemical Engineering Journal,2006,27(3):225-239.
[8]HUANG S,TAN K K,LEE T H,et al.Adaptive control of mechanical systems using neural networks[J].IEEE Transactions on Systems,Man and Cybernetics Part C(Applications and Reviews),2007,37(5):897-903.
[9]席裕庚.动态不确定环境下广义控制问题的预测控制[J].控制理论与应用,2000,17(5):665-670.
[10]FAVOREEL W,DE MOOR B,GEVERS M.SPC:Subspace predictive control[J].IFAC Proceedings Volumes,1999,32(2):4004-4009.
[11]MORARI M,LEE J H.Model predictive control:past,present and future[J].Computers&Chemical Engineering,1999,23(4-5):667-682.
[12]DONG J,VERHAEGEN M,HOLWEG E.Closed-loop subspace predictive control for fault tolerant MPC design[J].IFACProceedings Volumes,2008,41(2):3216-3221.
[13]HALLOUZI R,VERHAEGEN M.Fault-tolerant subspace predictive control applied to a Boeing 747 model[J].Journal of Guidance,Control,and Dynamics,2008,31(4):873-883.
[14]VAN OVERSCHEE P,DE MOOR B L.Subspace identification for linear systems:Theory-Implementation-Applications[J].Springer Science&Business Media,2012,23(4):236-252.
[15]LI W,HAN Z,SHAH S L.Subspace identification for FDI in systems with non-uniformly sampled multirate data[J].Automatica,2006,42(4):619-627.
[16]杨华,李少远.一种完全数据驱动的子空间辨识与鲁棒预测控制器设计[J].控制理论与应用,2007,24(5):732-736.
[17]KADALI R,HUANG B,ROSSITER A.A data driven subspace approach to predictive controller design[J].Control Engineering Practice,2003,11(3):261-278.
[18]LAZAR M,HEEMELS W,WWILAND S,et al.Stabilizing model predictive control of hybrid systems[J].IEEE Transactions on Automatic Control,2006,51(11):1813-1818.
[19]YANG H,LI S.Subspace-based adaptive predictive control for a class of nonlinear systems[J].International Journal of Innovative Computing,Information and Control,2005,1(4):743-753.