Работа магистра наук - Интеллектуальное управление стабилизацией подсистем башни при наличии возмущений от пересеченной местности. Gümüsay Ö. Intelligent stabilization control of turret subsystems under disturbances from unstructured terrain

167 Pages·4.262 MB·Russian

INTELLIGENT STABILIZATION CONTROL OF TURRET
SUBSYSTEMS UNDER DISTURBANCES FROM
UNSTRUCTURED TERRAIN

A THESIS SUBMITTED TO
THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
OF
MIDDLE EAST TECHNICAL UNIVERSITY

BY

ÖZDEMİR GÜMÜŞAY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR
THE DEGREE OF MASTER OF SCIENCE
IN
ELECTRICAL AND ELECTRONICS ENGINEERING

NOVEMBER 2006
Approval of the Graduate School of Natural and Applied Sciences

Prof. Dr. Canan ÖZGEN
Director

I certify that this thesis satisfies all the requirements as a thesis for the degree of
Master of Science.

Prof. Dr. İsmet ERKMEN
Head of Department

This is to certify that I have read this thesis and that in our opinion it is fully
adequate, in scope and quality, as a thesis for the degree of Master of Science.

Prof. Dr. İsmet ERKMEN Prof. Dr. Aydan M. ERKMEN
Co-Supervisor Supervisor

Examining Committee Members

Prof. Dr. İsmet ERKMEN (METU, EE)
Prof. Dr. Aydan M. ERKMEN (METU, EE)
Prof. Dr. Aydın ERSAK (METU, EE)
Prof. Dr. Tuna BALKAN (METU, ME)
İlhan BAŞÇUHADAR (M.S.) (ASELSAN)
I hereby declare that all information in this document has been obtained and
presented in accordance with academic rules and ethical conduct. I also
declare that, as required by these rules and conduct, I have fully cited and
referenced all material and results that are not original to this work.

Name, Last Name: Özdemir GÜMÜŞAY

Signature:

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ABSTRACT

INTELLIGENT STABILIZATION CONTROL OF TURRET
SUBSYSTEMS UNDER DISTURBANCES FROM
UNSTRUCTURED TERRAIN

Gümüşay, Özdemir
M.Sc., Department of Electrical and Electronics Engineering
Supervisor: Prof. Dr. Aydan M. ERKMEN
Co-Supervisor: Prof. Dr. İsmet ERKMEN

November 2006, 147 Pages

In this thesis, an intelligent controller for gun and/or sight stabilization of turret
subsystems is developed using artificial neural networks. A classical proportional,
integral and derivative (PID) controller equipped with a non-linear unbalance
compensation algorithm is used as the low-level controller. The gains of this PID
controller are tuned using a multilayered back-propagation neural network. These
gains are modeled as a function of the error between the command and feedback
signals and this model is generated by the function fitting property of neural
networks as an estimate. The network is called as the “Neural PID Tuner” and it
takes the current and previous errors as inputs and outputs the PID gains of the
controller.
Columb friction is the most important non-linearity in turret subsystems that
heavily lower the efficiency of the controller. Another multilayered back-
propagation neural network is used in order to increase the performance of the PID
controller by identifying and compensating this Columb friction. This network
utilizes the error between the output of the PID controller driving the physical

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system with Columb friction and the output of the identical PID controller driving
a virtual equivalent linear system without Columb friction. The linear dynamics of
the physical system is identified using a single layer linear neural network with
pure linear activation function and the equivalent virtual linear system is emulated
using this identification.
The proposed methods are applied to both computer simulations and hardware
experimental setup. In addition, sensitivity and performance analysis are
performed both by using the mathematical model and hardware experimental
setup.

Keywords: Intelligent Control, Neural Networks, Neural PID Tuner, Neural
Friction Compensation, Stabilization Control.

v
ÖZ

HERHANGİ BİR ARAZİDEN GELEN BOZUCU ETKİLER
ALTINDAKİ TARET ALT SİSTEMLERİNİN AKILLI
STABİLİZASYON DENETİMİ

GÜMÜŞAY, Özdemir
Yüksek Lisans, Elektrik ve Elektronik Mühendisliği Ana Bilim Dalı
Tez Yöneticisi: Prof. Dr. Aydan M. ERKMEN
Ortak Tez Yöneticisi: Prof. Dr. İsmet ERKMEN

Kasım 2006, 147 Sayfa

Bu çalışmada taret alt sistemlerinin silah ve/veya görüş stabilizasyonu için yapay
sinir ağları kullanılarak akıllı bir denetleç tasarlanmıştır. Alt seviye denetimde
doğrusal olmayan bozuk balansın da giderildiği klasik bir oransal, integral ve türev
denetleci (PID) kullanılmıştır. Bu denetlecin kazançları çok katmanlı bir geri
iletimli sinir ağı ile eniyilenmiştir. Bu denetleç parametreleri istek ve geri besleme
sinyalleri arasındaki hatanın bir fonksiyonu olarak modellenmiş ve bu fonksiyon
yapay sinir ağlarının fonksiyon yakınsama özelliği kullanılarak tahmin edilmiştir.
Bu ağ “Sinirsel PID Eniyileyici” olarak adlandırılmıştır ve giriş olarak denetim
hatasının şu anki ve geçmiş değerlerini almakta ve denetleç kazançlarını çıkış
olarak vermektedir.
Kuru sürtünme taret alt sistemlerinde denetim performansını düşüren doğrusal
olmayan en önemli etmendir. PID denetlecinin performansını artırmak için başka
birçok katmanlı geri iletimli sinir ağı kuru sürtünmenin öğrenilmesi ve giderilmesi
için kullanılmıştır. Bu ağ sürtünmeyi öğrenmek için kuru sürtünme etkisinde olan
fiziksel sistemi süren PID denetlecinin çıkışı ile kuru sürtünme etkisinde olmayan
ve fiziksel sistemin doğrusal bir eşleniği olan yapay sistemi süren PID denetlecinin

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çıkışı arasındaki hatadan yararlanmaktadır. Fiziksel sistemin doğrusal davranışı ise
tek katmanlı doğrusal bir sinir ağı ile tanılanmaktadır ve yapay doğrusal sistem bu
doğrusal tanılama sonucunu kullanmaktadır.
Son olarak, önerilen metot bilgisayar simülasyonlarına ve deneysel bir prototipe
uygulanmış ve sinir ağlarının hassasiyet ve performans analizleri yapılmıştır.

Anahtar Kelimeler: Akıllı Denetim, Yapay Sinir Ağları, Sinirsel PID İyileyici,
Sinirsel Sürtünme Giderici, Stabilizasyon Denetimi.

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To My Wife…

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ACKNOWLEDGEMENTS

I express my sincere appreciation to my thesis supervisor Prof. Dr. Aydan
ERKMEN for her supervision, support and helpful critics throughout the progress
of my thesis study.

I would like to thank to Mustafa Burak GÜRCAN, Ömer GÖKSU and Serkan
GÜVEY for their grateful support and for their valuable commands.

The cooperation and friendly support of my colleagues in ASELSAN during my
thesis study also deserves to be acknowledged.

Finally, many thanks to my wife for her endless support and for her immortal love.

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TABLE OF CONTENTS

PLAGIARISM……....………………………..…………...…..………...…........ III
ABSTRACT...........................................................................................................IV
ÖZ..........................................................................................................................VI
ACKNOWLEDGEMENTS...................................................................................IX
TABLE OF CONTENTS........................................................................................X
LIST OF SYMBOLS & ABBREVIATIONS......................................................XX
1. INTRODUCTION...........................................................................................1
1.1 INTELLIGENT STABILIZATION CONTROL – MOTIVATION.................................1
1.2 OBJECTIVE AND GOALS OF THE STUDY...........................................................3
1.3 METHODOLOGY...............................................................................................4
1.4 CONTRIBUTIONS OF THE THESIS......................................................................5
1.5 OUTLINE OF THE THESIS..................................................................................6
2. LITERATURE SURVEY................................................................................7
2.1 BATTLE TANKS AND TURRET SUBSYSTEMS.....................................................7
2.2 GUN STABILIZATION.....................................................................................10
2.3 STABILIZATION CONTROLLERS......................................................................14
2.4 FRICTION MODELING AND COMPENSATION...................................................17
2.5 SYSTEM IDENTIFICATION AND SYSTEM EMULATION.....................................19
2.6 MATHEMATICAL BACKGROUND....................................................................20
2.6.1 Intelligent Control.................................................................................20
2.6.2 Artificial Neural Networks....................................................................22
3. PROPOSED CONTROLLER ARCHITECTURE........................................30
3.1 LOW-LEVEL STABILIZATION CONTROLLER...................................................31
3.2 NEURAL PID TUNER .....................................................................................34
3.2.1 Mathematical Derivations for Neural PID Tuner Network ..................36
3.2.2 Design of the Neural PID Tuner Network............................................41
3.3 NEURAL FRICTION COMPENSATOR................................................................54

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