Data-driven Time-delay Estimation

Time-delay is widespread in industrial process sites.Delay parameters have an important influence on the performance of the control system and are one of the main parameters considered in the design and integration of the control system.The problem of delay estimation therefore becomes an integral part of the identification and design of control systems.How to obtain accurate time delay estimates with minimal a priori knowledge is still of interest to scholars and engineers.In this paper,a data-driven time-delay parameter estimation method and its implementation are investigated for the estimation of time-delay parameters in industrial processes.The main research work in this paper can be summarized as follows.First,a pure data-driven time-delay estimation method and algorithm implementation are investigated.The closed-loop impulse response model is obtained by the FCOR modeling of the closed-loop output data,which is transformed and calculated for correlation analysis;the peaks in the correlation function are observed and judged;the contrast changes of the peaks in the autocorrelation function formed by the filtering method and filter parameter selection are used to exclude the influence of the interference peaks on the delay estimation and obtain accurate estimates of the delay parameters.The simulation cases are compared with other time delay estimation algorithms to verify the validity and feasibility of the proposed algorithm in this paper.Second,the implementation of this paper's algorithm in the industrial field is studied.On the one hand,the effect of non-integer sampling periods in simulated continuous control systems on the delay estimation of discrete systems is analyzed;on the other hand,the estimation accuracy of the delay estimation of the algorithm proposed in this paper is analyzed and the error range of the delay estimation is given.For the discrete model of the sampling control system,simulation validation was performed.