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Abstract: We examine the problem of obtaining adaptive control laws which tune themselves to control laws minimizing the variance of the tracking error between the output of the linear ARMAX system and a speci ed reference trajectory. If the reference trajectory is suciently rich of order greater than or equal to the sum of the degrees of the control and noise polynomials in the ARMAX system, then an adaptive controller is exhibited for which the parameter estimates are strongly consistent. For the... (Update)
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BibTeX entry: (Update)
P. R. Kumar and L. Praly, \Self-tuning trackers," SIAM Journal on Control and Optimization, vol. 25, no. 4, pp. 1053-1071, 1987. http://citeseer.ist.psu.edu/kumar87selftuning.html More
@article{ kumar87selftuning,
author = "P. R. Kumar and L. Praly",
title = "Self-tuning trackers",
journal = "SIAM Journal on Control and Optimization",
volume = "25",
number = "4",
month = "????",
pages = "1053--1071",
year = "1987",
url = "citeseer.ist.psu.edu/kumar87selftuning.html" }
Citations (may not include all citations):101 Adaptive Filtering Prediction and Control (context) - Goodwin, Sin - 1984
70 Almost Sure Convergence (context) - Stout - 1974
61 Analysis of recursive stochastic algorithms (context) - Ljung - 1977
18 On self-tuning regulators (context) - Astr, Wittenmark - 1973
15 On Positive real transfer functions and the convergence of s.. (context) - Ljung - 1977
12 Adaptive control with the stochastic approximation algorithm.. (context) - Becker, Kumar et al. - 1985
10 Discrete time stochastic adaptive control (context) - Goodwin, Ramadge et al. - 1981
7 Identi- cation and Adaptive Control (context) - Kumar, Varaiya et al. - 1986
6 Least squares estimates in stochastic regression with applic.. (context) - Lai, Wei - 1982
1 Control by stochastic approximation (context) - Wei - 1984
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