Comparison of six on-line identification and parameter estimation methods

  • Authors:

  • R. Isermann;U. Baur;W. Bamberger;P. Kneppo;H. Siebert

  • Affiliations:

  • Group of Automatic Control and Process Dynamics, Department of Power Engineering, University Stuttgart, Keplerstr. 17, 7000 Stuttgart, W. Germany;Group of Automatic Control and Process Dynamics, Department of Power Engineering, University Stuttgart, Keplerstr. 17, 7000 Stuttgart, W. Germany;Group of Automatic Control and Process Dynamics, Department of Power Engineering, University Stuttgart, Keplerstr. 17, 7000 Stuttgart, W. Germany;Group of Automatic Control and Process Dynamics, Department of Power Engineering, University Stuttgart, Keplerstr. 17, 7000 Stuttgart, W. Germany;Group of Automatic Control and Process Dynamics, Department of Power Engineering, University Stuttgart, Keplerstr. 17, 7000 Stuttgart, W. Germany

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 1974

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Abstract

In order to compare and evaluate identification and parameter estimation methods, three simulated linear discrete processes-a second-order oscillatory process, a second-order nonminimum phase process, and a third-order, low pass, process with delay-were identified with the following on-line methods. 1.(1) Least squares 2.(2) Generalized least squares 3.(3) Instrumental variables 4.(4) Stochastic approximation 5.(5) Correlation analysis with least squares parameter estimation 6.(6) Fourier analysis using a model with three unknown parameters. The test processes correspond to the ''multi solution test cases'' we have proposed for the 3rd IFAC-Symposium on Identification and System Parameter Estimation. The variances of parameter estimates and impulse responses are given for simulation runs using all six parameter estimation methods for two noise-to-signal ratios and three measurement time periods. The system order is assumed to be known exactly. Since the ultimate application of the parameter estimates is important in choosing an identification procedure, on example of a final goal, the design of a digital control algorthm, was chosen for these evaluations. Hence the identified models are used to optimize the parameters of a three-mode controller. Both the controller parameters and the r.m.s. error of the closed loop controlled variable are compared for the identified and the exact model.