About: Self-tuning Predictive Control of Nonlinear Servo-motor     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • The paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo ? motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model ? Amira DR300.
  • The paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo ? motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model ? Amira DR300. (en)
Title
  • Self-tuning Predictive Control of Nonlinear Servo-motor
  • Self-tuning Predictive Control of Nonlinear Servo-motor (en)
skos:prefLabel
  • Self-tuning Predictive Control of Nonlinear Servo-motor
  • Self-tuning Predictive Control of Nonlinear Servo-motor (en)
skos:notation
  • RIV/70883521:28140/10:63508985!RIV11-MSM-28140___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0567), Z(MSM7088352101)
http://linked.open...iv/cisloPeriodika
  • 6
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 286844
http://linked.open...ai/riv/idVysledku
  • RIV/70883521:28140/10:63508985
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Non-linear system; servo-system; CARIMA model; self-tuning control; predictive control; real-time control (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • SK - Slovenská republika
http://linked.open...ontrolniKodProRIV
  • [3E348E549B89]
http://linked.open...i/riv/nazevZdroje
  • Journal of Electrical Engineering
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 61
http://linked.open...iv/tvurceVysledku
  • Bobál, Vladimír
  • Chalupa, Petr
  • Dostál, Petr
  • Kubalčík, Marek
http://linked.open...n/vavai/riv/zamer
issn
  • 1335-3632
number of pages
http://localhost/t...ganizacniJednotka
  • 28140
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 47 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software