TAILIEUCHUNG - Báo cáo "An application of random process for controlled object identification with traffic delay problem "

In the article proposed an effective method estimating transfer function model of controlled plant including dead-time delay, based on stochatstic time series of input-output signals. The model structure is modified with parameters optimized until the model error becomes "white-noise" series that with inough smal auto-correlation function. | VNU Journal of Science Mathematics - Physics 24 2008 101-109 An application of random process for controlled object identification with traffic delay problem Vu Tien Viet Department of Mathematics Mechanics Informatics College of Science VNU 334 Nguyen Trai Hanoi Vietnam Received 23 January 2007 received in revised form 20 March 2008 Abstract. In the article proposed an effective method estimating transfer function model of controlled plant including dead-time delay based on stochatstic time series of input-output signals. The model structure is modified with parameters optimized until the model error becomes white-noise series that with inough smal auto-correlation function. 1. Propose The Real signals which occur in the control process always imlpy influences of many random factors so the Directive Object Identification Problem is often related to random process. Mathematically the Controlled Object Identification problem is the problem that predicts the trend of Random Process y t f t u v t where t - time u - vector of non-random input variables f t u - regressive function that reflects the trend of non-random process or is the model of the identification problem v t - random error. The Theory of Prediction and Identification has been studied and developed with thousands of scientific works made public since last century. We can find the fundamental results of studies of statistics and prediction in 1 2 of kinetics system identification in detail in 3 4 . To use linear algebra methods we often try to change the regressive models into linear combination forms of coefficients f t u V I cifi t u where ci - parameters fi t u - given component functions. By using this model the Parameter Identification Problem can be solved easily. However this model is not used to solve the analysis and synthesise problem of systems and we have to transform this model into the form of sets of state equations sets of Cauchy differential equations or transfer function form. There is a

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