Self-tuning control based on generalized minimum variance criterion for auto-regressive models

Automatica ◽  
2008 ◽  
Vol 44 (8) ◽  
pp. 1970-1975 ◽  
Author(s):  
Anna Patete ◽  
Katsuhisa Furuta ◽  
Masayoshi Tomizuka
2007 ◽  
Vol 40 (13) ◽  
pp. 411-416 ◽  
Author(s):  
Anna Patete ◽  
Katsuhisa Furuta ◽  
Masayoshi Tomizuka

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.


2020 ◽  
Vol 47 (8) ◽  
pp. 0804005
Author(s):  
田晓东 Tian Xiaodong ◽  
汪毅 Wang Yi ◽  
杨晋 Yang Jin ◽  
金浩 Jin Hao ◽  
蔡怀宇 Cai Huaiyu ◽  
...  

Weed Science ◽  
1995 ◽  
Vol 43 (3) ◽  
pp. 394-401 ◽  
Author(s):  
Karen A. Garrett

When a range of weed densities is needed for competition experiments, one method of reducing existing populations is to remove all weeds in strips of specified length down the crop row. This technique also can be used to create different sizes of weed dusters. Two methods of sampling weed densities after such thinning were compared:unrestricted sampling(under which quadrats are placed randomly within the row) andrestricted sampling(under which quadrats are randomly placed within a row under the restriction that they coincide with the beginning of an infested strip). The bias of estimators of weed density under each of these two approaches was derived and bias-corrected estimators of weed density from the two methods were compared on the basis of their variance. The variance under restricted sampling is less than or equal to the variance under unrestricted sampling so that, by a minimum variance criterion, restricted sampling using a bias correction is the better technique.


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