Cover Image

Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller

Fuzzy logic concept was first conceived by Lotfi Zadeh in 1965 by incorporating rule based approach to solve control problems. The advantage of Fuzzy Logic Controller (FLC) is that the control process can be controlled without knowing much knowledge of their dynamics. FLC is applied as the controlle...

Full description

Saved in:
Bibliographic Details
Main Author: Zaridah, Mat Zain
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis 2010
Subjects:
Fuzzy logic controller (FLC)
Pico-statellite attitude
Aerospace
Statellite system
Adaptive Predictive Fuzzy Logic Controller (APFLC)
Genetic algorithm (GA)
Attitude control
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/9883
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-9883
record_format dspace
spelling my.unimap-98832010-10-19T03:17:09Z Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller Zaridah, Mat Zain Fuzzy logic controller (FLC) Pico-statellite attitude Aerospace Statellite system Adaptive Predictive Fuzzy Logic Controller (APFLC) Genetic algorithm (GA) Attitude control Fuzzy logic concept was first conceived by Lotfi Zadeh in 1965 by incorporating rule based approach to solve control problems. The advantage of Fuzzy Logic Controller (FLC) is that the control process can be controlled without knowing much knowledge of their dynamics. FLC is applied as the controller to most of commercial mercantile products in past 25 years. Since that, many applications of the FLC in controlling the Pico-satellite’s attitude have been proposed successfully. In this regards, a new method of Pico-satellite attitude control using Mamdani Fuzzy Logic Principles is introduced. The design of the APFLC is initially started with the designation of Basic FLC with two input and single output system. Then, a Predictive FLC is designed to compensate the effects of delay time which occurs in the Pico-satellite control system. The predictor is a one step-ahead predictor which estimates the required control at the next sampling time and applies to the system at current sampling time. Finally the adaptive portion of FLC is applied in order to compensate the effect of unknown parameter variations in the Pico-satellite system by using an adaptable gain which is connected in the forward path of the FLC. The response of the Pico-satellite is compared with a model reference adaptive system, derived on the basis of deviation in the responses and updates the adaptive gain. The adaptation continues until the Pico-satellite attitude reaches the set-reference attitude. The design schemes of modeling adaptive and predictive FLC (APFLC) is described as follow: Basic FLC, Predictive FLC (PFLC) and APFLC. The APFLC is compared with a conventional Proportional Integral Derivative (PID) controller. The simulation results are presented and the output responses indicate that this approach of FLC is acceptable even in the case of a Pico-satellite subjected to input noise, measurement noises, intermittent disturbances and also with sensor nonlinearity. It is observed that the APFLC showed convincing performance over the entire simulation of the Pico-satellite. Genetic Algorithm (GA) is a computational model inspired by evaluation. This algorithm encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to this structure to preserve critical information. The contribution of this work is to optimize the base of Fuzzy membership function of the APFLC by using GA technique. The optimization technique involved from two points to four points and end with six points. The performances obtained show that the optimized APFLC is better than the non-optimize APFLC in terms of RMSE and the settling time. 2010-10-19T03:17:09Z 2010-10-19T03:17:09Z 2009 Thesis http://hdl.handle.net/123456789/9883 en Universiti Malaysia Perlis School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Fuzzy logic controller (FLC)
Pico-statellite attitude
Aerospace
Statellite system
Adaptive Predictive Fuzzy Logic Controller (APFLC)
Genetic algorithm (GA)
Attitude control
spellingShingle Fuzzy logic controller (FLC)
Pico-statellite attitude
Aerospace
Statellite system
Adaptive Predictive Fuzzy Logic Controller (APFLC)
Genetic algorithm (GA)
Attitude control
Zaridah, Mat Zain
Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
description Fuzzy logic concept was first conceived by Lotfi Zadeh in 1965 by incorporating rule based approach to solve control problems. The advantage of Fuzzy Logic Controller (FLC) is that the control process can be controlled without knowing much knowledge of their dynamics. FLC is applied as the controller to most of commercial mercantile products in past 25 years. Since that, many applications of the FLC in controlling the Pico-satellite’s attitude have been proposed successfully. In this regards, a new method of Pico-satellite attitude control using Mamdani Fuzzy Logic Principles is introduced. The design of the APFLC is initially started with the designation of Basic FLC with two input and single output system. Then, a Predictive FLC is designed to compensate the effects of delay time which occurs in the Pico-satellite control system. The predictor is a one step-ahead predictor which estimates the required control at the next sampling time and applies to the system at current sampling time. Finally the adaptive portion of FLC is applied in order to compensate the effect of unknown parameter variations in the Pico-satellite system by using an adaptable gain which is connected in the forward path of the FLC. The response of the Pico-satellite is compared with a model reference adaptive system, derived on the basis of deviation in the responses and updates the adaptive gain. The adaptation continues until the Pico-satellite attitude reaches the set-reference attitude. The design schemes of modeling adaptive and predictive FLC (APFLC) is described as follow: Basic FLC, Predictive FLC (PFLC) and APFLC. The APFLC is compared with a conventional Proportional Integral Derivative (PID) controller. The simulation results are presented and the output responses indicate that this approach of FLC is acceptable even in the case of a Pico-satellite subjected to input noise, measurement noises, intermittent disturbances and also with sensor nonlinearity. It is observed that the APFLC showed convincing performance over the entire simulation of the Pico-satellite. Genetic Algorithm (GA) is a computational model inspired by evaluation. This algorithm encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to this structure to preserve critical information. The contribution of this work is to optimize the base of Fuzzy membership function of the APFLC by using GA technique. The optimization technique involved from two points to four points and end with six points. The performances obtained show that the optimized APFLC is better than the non-optimize APFLC in terms of RMSE and the settling time.
format Thesis
author Zaridah, Mat Zain
author_facet Zaridah, Mat Zain
author_sort Zaridah, Mat Zain
title Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
title_short Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
title_full Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
title_fullStr Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
title_full_unstemmed Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller
title_sort modeling and control of a pico-satellite attitude using fuzzy logic controller
publisher Universiti Malaysia Perlis
publishDate 2010
url http://dspace.unimap.edu.my/xmlui/handle/123456789/9883
_version_ 1643789611272503296
score 13.222552

Similar Items