Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation

Controlling a complex dynamic object in real time and satisfying multiple criteria involves constant dealing with state deviations from the constructed plan, caused by external disturbances of a stochastic nature, and the lack of knowledge about the controlled object in the process of building the p...

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Main Authors: Andrey Gnidenko, Vladislav Sobolevsky, Valerity Zakharov
Format: Article
Language:English
Published: FRUCT 2019-04-01
Series:Proceedings of the XXth Conference of Open Innovations Association FRUCT
Subjects:
Online Access:https://fruct.org/publications/abstract24/files/Gni.pdf
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spelling doaj-2880e3c5986149218ea368413e0c3db92020-11-24T21:31:45ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372019-04-0185424608612Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance CompensationAndrey Gnidenko0Vladislav Sobolevsky1Valerity Zakharov2St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Saint Petersburg, RussiaSt. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Saint Petersburg, RussiaSt. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Saint Petersburg, RussiaControlling a complex dynamic object in real time and satisfying multiple criteria involves constant dealing with state deviations from the constructed plan, caused by external disturbances of a stochastic nature, and the lack of knowledge about the controlled object in the process of building the plan. The present paper suggests models and algorithms for compensating such deviations in relation to the control of individual trains within the framework of the developed multi- level multi-model complex designed for group planning and coordination of trains within a single logistics network. Another covered aspect is the algorithm of parametric adaptation, involving adjustment of model parameters and improvement of planning accuracy.https://fruct.org/publications/abstract24/files/Gni.pdf multi-criteria optimizationoptimal controlsolution constraintscomplex planningproactive controlparametric adaptation
collection DOAJ
language English
format Article
sources DOAJ
author Andrey Gnidenko
Vladislav Sobolevsky
Valerity Zakharov
spellingShingle Andrey Gnidenko
Vladislav Sobolevsky
Valerity Zakharov
Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation
Proceedings of the XXth Conference of Open Innovations Association FRUCT
multi-criteria optimization
optimal control
solution constraints
complex planning
proactive control
parametric adaptation
author_facet Andrey Gnidenko
Vladislav Sobolevsky
Valerity Zakharov
author_sort Andrey Gnidenko
title Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation
title_short Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation
title_full Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation
title_fullStr Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation
title_full_unstemmed Methods and Algorithms of Proactive control of Complex Dynamic Objects with Disturbance Compensation
title_sort methods and algorithms of proactive control of complex dynamic objects with disturbance compensation
publisher FRUCT
series Proceedings of the XXth Conference of Open Innovations Association FRUCT
issn 2305-7254
2343-0737
publishDate 2019-04-01
description Controlling a complex dynamic object in real time and satisfying multiple criteria involves constant dealing with state deviations from the constructed plan, caused by external disturbances of a stochastic nature, and the lack of knowledge about the controlled object in the process of building the plan. The present paper suggests models and algorithms for compensating such deviations in relation to the control of individual trains within the framework of the developed multi- level multi-model complex designed for group planning and coordination of trains within a single logistics network. Another covered aspect is the algorithm of parametric adaptation, involving adjustment of model parameters and improvement of planning accuracy.
topic multi-criteria optimization
optimal control
solution constraints
complex planning
proactive control
parametric adaptation
url https://fruct.org/publications/abstract24/files/Gni.pdf
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AT vladislavsobolevsky methodsandalgorithmsofproactivecontrolofcomplexdynamicobjectswithdisturbancecompensation
AT valerityzakharov methodsandalgorithmsofproactivecontrolofcomplexdynamicobjectswithdisturbancecompensation
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