NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER
The aim of this work is the synthesis of neural network aiming and stabilization system for the special equipment of moving objects with neuro-controller on the basis of standard model and performance comparison of the neural network system with the neural network predictive control. Build a block d...
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| Datum: | 2015 |
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| Format: | Artikel |
| Sprache: | Ukrainisch |
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National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine
2015
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| Online Zugang: | http://eie.khpi.edu.ua/article/view/2074-272X.2015.4.06 |
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| Назва журналу: | Electrical Engineering & Electromechanics |
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Electrical Engineering & Electromechanics| _version_ | 1856543203206889472 |
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| author | Kuznetsov, B. I. Vasilets, T. E. Varfolomiyev, O. O. |
| author_facet | Kuznetsov, B. I. Vasilets, T. E. Varfolomiyev, O. O. |
| author_sort | Kuznetsov, B. I. |
| baseUrl_str | |
| collection | OJS |
| datestamp_date | 2016-03-09T07:37:42Z |
| description | The aim of this work is the synthesis of neural network aiming and stabilization system for the special equipment of moving objects with neuro-controller on the basis of standard model and performance comparison of the neural network system with the neural network predictive control. Build a block diagram of the neural network aiming and stabilization system, based on the subject control principle with PD-regulator in the position loop and with neuro-controller on the basis of standard model in the in the velocity loop. The neuro-controller on the basis of standard model Model Reference Controller is synthesized in the MATLAB Neural Network Toolbox and system simulation is performed. The studies show that the transient state variables of the system are oscillatory. Therefore, the neuro-controller with the prediction NN Predictive Controller should be used for aiming and stabilizing system to provide high dynamic characteristics achieved at the cost of higher complexity and computational cost. |
| first_indexed | 2025-07-17T11:45:28Z |
| format | Article |
| id | eiekhpieduua-article-48731 |
| institution | Electrical Engineering & Electromechanics |
| language | Ukrainian |
| last_indexed | 2025-07-17T11:45:28Z |
| publishDate | 2015 |
| publisher | National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine |
| record_format | ojs |
| spelling | eiekhpieduua-article-487312016-03-09T07:37:42Z NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER НЕЙРОСЕТЕВАЯ СИСТЕМА НАВЕДЕНИЯ И СТАБИЛИЗАЦИИ С РЕГУЛЯТОРОМ НА ОСНОВЕ ЭТАЛОННОЙ МОДЕЛИ MODEL REFERENCE CONTROLLER НЕЙРОМЕРЕЖЕВА СИСТЕМА НАВЕДЕННЯ І СТАБІЛІЗАЦІЇ З РЕГУЛЯТОРОМ НА ОСНОВІ ЕТАЛОННОЇ МОДЕЛІ MODEL REFERENCE CONTROLLER Kuznetsov, B. I. Vasilets, T. E. Varfolomiyev, O. O. neural network control aiming and stabilization system nonlinear dynamic object neuro-controller on the basis of standard model Model Reference Controller 681.5.01.23 нейросетевое управление система наведения и стабилизации нелинейный динамический объект нейрорегулятор на основе эталонной модели Model Reference Controller 681.5.01.23 нейромережеве управління система наведення і стабілізації нелінійний динамічний об’єкт нейрорегулятор на основі еталонної моделі Model Reference Controller 681.5.01.23 The aim of this work is the synthesis of neural network aiming and stabilization system for the special equipment of moving objects with neuro-controller on the basis of standard model and performance comparison of the neural network system with the neural network predictive control. Build a block diagram of the neural network aiming and stabilization system, based on the subject control principle with PD-regulator in the position loop and with neuro-controller on the basis of standard model in the in the velocity loop. The neuro-controller on the basis of standard model Model Reference Controller is synthesized in the MATLAB Neural Network Toolbox and system simulation is performed. The studies show that the transient state variables of the system are oscillatory. Therefore, the neuro-controller with the prediction NN Predictive Controller should be used for aiming and stabilizing system to provide high dynamic characteristics achieved at the cost of higher complexity and computational cost. Выполнен синтез системы наведения и стабилизации с применением нейросетевого регулятора на основе эталонной модели Model Reference Controller. Приведена структура и принцип действия нейрорегулятора. Разработана схема системы наведения и стабилизации с пропорционально-дифференциальным регулятором в контуре положения и нейросетевым регулятором в контуре скорости. Проведено моделирование нейросетевой системы на ЭВМ и выполнен анализ динамических характеристик системы. Виконано синтез системи наведення і стабілізації з застосуванням нейромережевого регулятора на основі еталонної моделі Model Reference Controller. Наведена структура і принцип дії нейрорегулятора. Розроблена схема системи наведення і стабілізації з пропорційнио-диференційним регулятором у контурі положення і нейромережевим регулятором у контурі швидкості. Проведено моделювання нейромережевої системи на ЕОМ і виконано аналіз динамічних характеристик системи. National Technical University "Kharkiv Polytechnic Institute" and Аnatolii Pidhornyi Institute of Power Machines and Systems of NAS of Ukraine 2015-08-28 Article Article application/pdf http://eie.khpi.edu.ua/article/view/2074-272X.2015.4.06 10.20998/2074-272X.2015.4.06 Electrical Engineering & Electromechanics; No. 4 (2015); 35-39 Электротехника и Электромеханика; № 4 (2015); 35-39 Електротехніка і Електромеханіка; № 4 (2015); 35-39 2309-3404 2074-272X uk http://eie.khpi.edu.ua/article/view/2074-272X.2015.4.06/44948 Copyright (c) 2015 B. I. Kuznetsov, T. E. Vasilets, O. O. Varfolomiyev https://creativecommons.org/licenses/by-nc/4.0 |
| spellingShingle | neural network control aiming and stabilization system nonlinear dynamic object neuro-controller on the basis of standard model Model Reference Controller 681.5.01.23 Kuznetsov, B. I. Vasilets, T. E. Varfolomiyev, O. O. NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER |
| title | NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER |
| title_alt | НЕЙРОСЕТЕВАЯ СИСТЕМА НАВЕДЕНИЯ И СТАБИЛИЗАЦИИ С РЕГУЛЯТОРОМ НА ОСНОВЕ ЭТАЛОННОЙ МОДЕЛИ MODEL REFERENCE CONTROLLER НЕЙРОМЕРЕЖЕВА СИСТЕМА НАВЕДЕННЯ І СТАБІЛІЗАЦІЇ З РЕГУЛЯТОРОМ НА ОСНОВІ ЕТАЛОННОЇ МОДЕЛІ MODEL REFERENCE CONTROLLER |
| title_full | NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER |
| title_fullStr | NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER |
| title_full_unstemmed | NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER |
| title_short | NEURO-SYSTEM OF AIMING AND STABILIZING WITH A REGULATOR ON THE BASIS OF STANDARD MODEL MODEL REFERENCE CONTROLLER |
| title_sort | neuro-system of aiming and stabilizing with a regulator on the basis of standard model model reference controller |
| topic | neural network control aiming and stabilization system nonlinear dynamic object neuro-controller on the basis of standard model Model Reference Controller 681.5.01.23 |
| topic_facet | neural network control aiming and stabilization system nonlinear dynamic object neuro-controller on the basis of standard model Model Reference Controller 681.5.01.23 нейросетевое управление система наведения и стабилизации нелинейный динамический объект нейрорегулятор на основе эталонной модели Model Reference Controller 681.5.01.23 нейромережеве управління система наведення і стабілізації нелінійний динамічний об’єкт нейрорегулятор на основі еталонної моделі Model Reference Controller 681.5.01.23 |
| url | http://eie.khpi.edu.ua/article/view/2074-272X.2015.4.06 |
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