Residence time regulation in chemical processes: Local optimal control realization by differential neural networks

Tatyana Poznyak, Isaac Chairez, Alexander Poznyak

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

Resumen

© Springer International Publishing AG, part of Springer Nature 2018. A new method to design local optimal controller for uncertain system governed by continuous flow transformations (CFT) is presented. The on-line solution of the adaptive gains adjusting a linear control form yields the calculus of the sub-optimal controller. A special performance index, oriented to solve the transient evolution of CFT systems, is proposed. The class of systems considered in this study is highly uncertain: some components of chemical reactions are no measurable on line and then, they cannot be used in the controller realization. The recovering of this information was executed by a differential neural network (DNN) structure. The ozonation process of a single contaminant (as the particular example of CFT) is evaluated in detail using the control design proposed here.
Idioma originalInglés estadounidense
Título de la publicación alojadaResidence time regulation in chemical processes: Local optimal control realization by differential neural networks
Páginas745-756
Número de páginas669
ISBN (versión digital)9783319925363
DOI
EstadoPublicada - 1 ene 2018
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2019 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10878 LNCS
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/19 → …

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