Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System
This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems....
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Stefan cel Mare University of Suceava
2007-04-01
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Online Access: | http://dx.doi.org/10.4316/AECE.2007.01004 |
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doaj-50fdb15e786146c89a77098b017001ed2020-11-24T23:13:03ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002007-04-01711822Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network SystemASTROV, I.TATARLY, S.TATARLY, S.This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.http://dx.doi.org/10.4316/AECE.2007.01004Control systemsguided missilemulti-rateneural networkssimulation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
ASTROV, I. TATARLY, S. TATARLY, S. |
spellingShingle |
ASTROV, I. TATARLY, S. TATARLY, S. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System Advances in Electrical and Computer Engineering Control systems guided missile multi-rate neural networks simulation |
author_facet |
ASTROV, I. TATARLY, S. TATARLY, S. |
author_sort |
ASTROV, I. |
title |
Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System |
title_short |
Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System |
title_full |
Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System |
title_fullStr |
Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System |
title_full_unstemmed |
Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System |
title_sort |
simulation of missile autopilot with two-rate hybrid neural network system |
publisher |
Stefan cel Mare University of Suceava |
series |
Advances in Electrical and Computer Engineering |
issn |
1582-7445 1844-7600 |
publishDate |
2007-04-01 |
description |
This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems. |
topic |
Control systems guided missile multi-rate neural networks simulation |
url |
http://dx.doi.org/10.4316/AECE.2007.01004 |
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