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Linear Parameter Varying Model Identification of Nonlinear Systems Without Steady State(PDF)

Journal of Xiamen University(Natural Science)[ISSN:0438-0479/CN:35-1070/N]

2017 04
Research Field:
Research Articles
Publishing date:


Linear Parameter Varying Model Identification of Nonlinear Systems Without Steady State
Article ID:
HUANG Jiangyin*ZHAO Jing
School of Electrical Engineering and Automation,Xiamen University of Technology,Xiamen 361024,China
nonlinear systems non-steady state linear parameter varying(LPV)model circulation fluidized bed boiler
CLC number:
TP 273
Document code:

Two kinds of identification methods of the linear parameter varying(LPV)models for the nonlinear systems without steady states are both proposed in this paper.For the LPV model with linear weights,because parameters only exist in the local linear models,Gauss-Newton method and least square method are combined to estimate all parameters.For the LPV model with Gaussian weights,parameters exist in both weighting functions and local linear models.Narendra-Gallman method is used to estimate parameters.In the method,all parameters are divided into linear and nonlinear parts according to the relationship between them and optimization objectives.Then these two parts are estimated using the alternating iterative method.The proposed algorithm is validated by identifying an industrial circulation fluidized bed boiler.The outputs of the LPV model and real process of three main outputs:steam pressure,steam temperature and furnace temperature are analyzed and compared.Best fittings of these outputs are increased by 52.8%,21.1% and 32.2% respectively,confirming the validity and practicability of the algorithm in the identification field of complex nonlinear industrial processes.


[1] CHEN L,TULSYAN A,HUANG B,et al.Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm[J].Journal of Process Control,2013,23(10):1480-1496.
[2] LJUNG L.Prediction error estimation methods[J].Circuits Systems & Signal Processing,2001,21(1):11-21.
[3] BARHAM R H,DRANE W.An algorithm for least squares estimation of nonlinear parameters when some of the parameters are linear[J].Technometrics,1972,14(3):757-766.
[4] HOPFIELD J J.Neural networks and physical systems with emergent collective computational abilities[J].Proceedings of the National Academy of Sciences of the United States of America,1982,79(8):2554-2558.
[5] SMOLAA J,SCH?LKOPF B.A tutorial on support vector regression[J].Statistics & Computing,2004,14(3):199-222.
[6] FRITSCHE C,OZKAN E,GUSTAFSSON F.Online EM algorithm for jump Markov systems[C]∥IEEE 15th International Conference on Information Fusion.Singapore:IEEE,2012:1941-1946.
[7] BANERJEE A,ARKUN Y,OGUNNAIKE B,et al.Estimation of nonlinear systems using linear multiple models[J].Aiche Journal,1997,43(5):1204-1226.
[8] ZHU Y C,XU Z H.A method of LPV model identification for control[J].IFAC Proceedings Volumes,2008,41(2):5018-5023.
[9] MORENO-BENITO M,FRANKL K,ESPU?A A,et al.A modeling strategy for integrated batch process development based on mixed-logic dynamic optimization[J].Computers & Chemical Engineering,2016,94:287-311.
[10] LOPEZ-SAUCEDO E S,GROSSMANN I E,SEGOVIA-HERNANDEZ J G,et al.Rigorous modeling,simulation and optimization of a conventional and nonconventional batch reactive distillation column:a comparative study of dynamic optimization approaches[J].Chemical Engineering Research & Design,2016,111:83-99.
[11] 黄江茵,赵晶.高纯度分馏塔的建模及其非线性控制[J].厦门大学学报(自然科学版),2016,55(2):251-258.
[12] HUANG J Y,JI G L,ZHU Y C,et al.Identification of multi-model LPV models with two scheduling variables [J].Journal of Process Control,2012,22(7):1198-1208.
[13] HARTLEY H O.The modified Gauss-Newton method for the fitting of non-linear regression functions by least squares[J].Technometrics,1961,3(2):269-280.
[14] NARENDRA K S,GALLMAN P G.An iterative method for the identification of nonlinear systems using Hammerstein model[J].IEEE Trans Autom Control,1966,11(3):546-550.
[15] JI G L,HUANG J Y,ZHANG K K,et al.Identification and predictive control for a circulation fluidized bed boiler[J].Knowledge-Based Systems,2013,45(3):62-75.
[16] ZHU Y C.Multivariable system identification for process control[M].London:Elsevier Science,2001.


收稿日期:2016-08-08 录用日期:2016-10-14
基金项目:福建省自然科学基金(2015J01275); 福建省教育厅省属高校科研项目(JK2015034)
Last Update: 1900-01-01