1985
DOI: 10.1080/00207178508933406
|View full text |Cite
|
Sign up to set email alerts
|

A self-tuning controller for systems with unknown or varying time delays†

Abstract: A SISO self-tuning controller with a Smith-predictor [ype of time delay compensator (STC-TDC) is proposed to handle open-loop stable processes with unknown or varying time delays. Two numerical examples comparing PI control, the standard STC of Clarke and Gawthrop, the self-tuning Dahlin algorithm of Vogel and Edgar, and the STC-TDC show that the new approach performs best for both set-point and load changes when process time delay changes occur. A third example shows that the STC-TDC matches the extended stab… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0
1

Year Published

1985
1985
2008
2008

Publication Types

Select...
4
3
3

Relationship

0
10

Authors

Journals

citations
Cited by 31 publications
(5 citation statements)
references
References 8 publications
0
4
0
1
Order By: Relevance
“…The measure node may be described by forward NN or dynamic recursive NN. Above all approximately range of the nucleus steelyard's measure lag is confirmed example for 6s 10s so lag time is 6s(equals tree control periods) as its principle is choosing the least value in range [7,8] , And then the forward NN is trained again and again according to the sample data structured by formula [y m ( k) y m (k-4), y m (k-5),y m (k-6),y m (k-7),u(k-4), u(k-5),u(k-6),u(k-7)] quoted from (1.1), so the high precision is obtained. For recursive NN, iteratively training the sample structured by [ y m ( k),y m (k-4),u(k-4)] also meets the need.…”
Section: Applicationmentioning
confidence: 99%
“…The measure node may be described by forward NN or dynamic recursive NN. Above all approximately range of the nucleus steelyard's measure lag is confirmed example for 6s 10s so lag time is 6s(equals tree control periods) as its principle is choosing the least value in range [7,8] , And then the forward NN is trained again and again according to the sample data structured by formula [y m ( k) y m (k-4), y m (k-5),y m (k-6),y m (k-7),u(k-4), u(k-5),u(k-6),u(k-7)] quoted from (1.1), so the high precision is obtained. For recursive NN, iteratively training the sample structured by [ y m ( k),y m (k-4),u(k-4)] also meets the need.…”
Section: Applicationmentioning
confidence: 99%
“…Die unbekannte Totzeit wird in [5; 6] zusammen mit anderen Parametern über Identifikationsalgorithmen geschätzt. In [7] wird die Totzeit auf einen minimalen Wert angenommen und die reale Totzeitschwankung durch die Schätzung der Parameter des überdimensionierten Zählerpolynoms berücksichtigt. Trotz des hohen Rechenaufwands in [5 bis 7] ist das Problem nur teilweise gelöst.…”
Section: Einführende üBersichtunclassified
“…The STR has been modified to account for variable dead-time 57 but has not been tested on the extrusion process. In controlling the extrudate dimension(s}, there exists a dead time between the die and the measuring device.…”
Section: Computer Control Of Extrusionmentioning
confidence: 99%