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Contribution to prior tuning of LQG selftuners. (English) Zbl 0757.93081
Summary: A prior prediction of control quality achievable by the optimally tuned LQG selftuner of a fixed structure is proposed. The prediction helps to judge in advance in advance the usefulness of the intended selftuner implementation. The proposed algorithmization makes also possible the off-line tuning of particular penalties in accordance with user’s wishes. In this way, the used Bayesian methodology (together with existing solution of structure determination problem) provides algorithmic tools for systematic pre-tuning of majority of user’s knobs.

93E20 Optimal stochastic control
LQG selftuner
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[1] G. J. Bierman: Factorization Methods for Discrete Sequential Estimation. Academic Press, New York 1977. · Zbl 0372.93001
[2] T. S. Fergusson: A Bayesian analysis of some nonparametric problems. Ann. Statist. 1 (1973), 209-230. · Zbl 0255.62037
[3] T. Jeníček: Prior Analysis of Expected Control Quality when Using Selftuning Controllers. (in Czech). Ph. D. Dissertation, Institute of Information Theory and Automation, Czecho- slovak Academy of Sciences, Prague 1988.
[4] M. Kárný, R. Kulhavý: Structure determination of regression-type models for adaptive prediction and control. Bayesian Analysis of Time Series and Dynamic Models (J. C Spall, Marcel Dekker, Inc., New York and Basel 1988.
[5] M. Kárný A. Halousková J. Böhm R. Kulhavý, P. Nedoma: Design of linear quadratic adaptive control: theory and algorithms for practice. Supplement to Kybernetika 21 (1981), No. 3-6.
[6] M. Kárný: Quantification of prior knowledge about global characteristics of linear models. Kybernetika 20 (1984), 376-385. · Zbl 0556.93070
[7] M. Kárný: Estimation of sampling period for selftuners. Submitted to 11th IFAC World Congress, Tallinn 1990.
[8] V. Kučera: Discrete Linear Control - The Polynomial Approach. J. Wiley, Chichester 1979.
[9] R. Kulhavý: SIC: User’s Guide (version 1.1). Institute of Information Theory and Automation, Czechoslovak Academy of Sciences, Prague 1988.
[10] P. Nedoma, R. Kulhavý: KOS: User’s Guide (version 1.1). Institute of Information Theory and Automation, Czechoslovak Academy of Sciences, Prague 1988.
[11] V. Peterka: Bayesian approach to system identification. Trends and Progress in System Identification (P. Eykhoff, Pergamon Press, Oxford 1981. · Zbl 0451.93059
[12] Preprints of 2nd IFAC Workshop on Adaptive Systems in Control and Signal Processing. 1-3 July, 1986, Lund, Sweden.
[13] Preprints of 4th IFAC Symposium on CAD in Control Systems. CAD’88, 23 - 25 Aug., 1988, Beijing, P. R. C
[14] R. C Rao: Linear Statistical Inference and Its Application. Second Edition. John Wiley, New York 1973.
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