Self-tuning minimum-variance regulators with adaptive determination of the model order

1985 ◽  
Author(s):  
Maciej Niedzwieoki
2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mourad Elloumi ◽  
Samira Kamoun

This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.


1976 ◽  
Vol 7 (5) ◽  
pp. 265-280
Author(s):  
N.A. Kartvelishvili ◽  
L.T. Gottschalk

It is assumed that the river runoff process can be approximated by a Markov process. The process is thus described by M distribution functions: Fn (qt, t ; qt-1; t-1;…;qt-n, t-n), t ≡ 1, 2, …, M where M is the number of time intervals within the year, n - the order of the Markov process and qt, in general, is a vector representing runoff at several sites in a river or neighbouring rivers. Fundamental hypothesis of relations between multivariate distributions and corresponding marginal distributions is given. A finite difference scheme for multisite and multilag generation of river runoff is derived. The derivation is based on the multivariate normal distribution. Different methods for determination of the order of the finite difference scheme are discussed as well as the influence of model order and method of parameter estimation on properties of the model.


1996 ◽  
Vol 29 (1) ◽  
pp. 5174-5179
Author(s):  
Roberto Horowitz ◽  
Bo Li ◽  
James McCormick

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