Optimal Control and Model Reduction Using a Finite-Interval H- Upsilon Criterion

1990 ◽  
Author(s):  
M. B. Subrahmanyam
Keyword(s):  
Optimal Control ◽  
Model Reduction ◽  
Finite Interval

scholarly journals Model Reduction for Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering

2018 ◽  
Vol 40 (4) ◽  
pp. B1055-B1079 ◽  
Author(s):  
Maria Strazzullo ◽  
Francesco Ballarin ◽  
Renzo Mosetti ◽  
Gianluigi Rozza
Keyword(s):  
Optimal Control ◽  
Model Reduction ◽  
Optimal Control Problems ◽  
Control Problems

scholarly journals Optimal Environmental Control for Indeterminate Greenhouse Crops

1997 ◽  
Author(s):  
Ido Seginer ◽  
James Jones ◽  
Per-Olof Gutman ◽  
Eduardo Vallejos
Keyword(s):  
Decision Making ◽  
Optimal Control ◽  
Model Reduction ◽  
Manufacturing Systems ◽  
Dry Matter ◽  
Environmental Control ◽  
Crop Model ◽  
State Variables ◽  
Large Computer ◽  
Greenhouse Crops

Increased world competition, as well as increased concern for the environment, drive all manufacturing systems, including greenhouses, towards high-precision operation. Optimal control is an important tool to achieve this goal, since it finds the best compromise between conflicting demands, such as higher profits and environmental concerns. The report, which is a collection of papers, each with its own abstract, outlines an approach for optimal, model-based control of the greenhouse environment. A reliable crop model is essential for this approach and a significant portion of the effort went in this direction, resulting in a radically new version of the tomato model TOMGRO, which can be used as a prototype model for other greenhouse crops. Truly optimal control of a very complex system requires prohibitively large computer resources. Two routes to model simplification have, therefore, been tried: Model reduction (to fewer state variables) and simplified decision making. Crop model reduction from nearly 70 state variables to about 5, was accomplished by either selecting a subset of the original variables or by forming combinations of them. Model dynamics were then fitted either with mechanistic relationships or with neural networks. To simplify the decision making process, the number of costate variables (control policy parametrs) was recuced to one or two. The dry-matter state variable was transformed in such a way that its costate became essentially constant throughout the season. A quasi-steady-state control algorithm was implemented in an experimental greenhouse. A constant value for the dry-matter costate was able to control simultaneously ventilation and CO2 enrichment by continuously producing weather-dependent optimal setpoints and then maintaining them closely.

Model reduction algorithms for optimal control and importance sampling of diffusions

Nonlinearity ◽  
2016 ◽  
Vol 29 (8) ◽  
pp. 2298-2326 ◽  
Author(s):  
Carsten Hartmann ◽  
Christof Schütte ◽  
Wei Zhang
Keyword(s):  
Optimal Control ◽  
Model Reduction ◽  
Importance Sampling
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