Adaptive neuro-controller design for nano-satellite attitude control

The motivation of this research is to bring the technology of spacecraft control into university education and to bring the possibility of developing our own satellite that will put us of equal standard with other developed nations. The purpose of this research is to develop the control scheme for...

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Main Author:	Norhayati, Mohd Nazid
Format:	Thesis
Language:	English
Subjects:	Spacecraft control Adaptive Neuro-Controller (ANC) Satellite Innovative Satellite (InnoSAT) Nano-satellite system
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/2/Full%20text.pdf
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id	my-unimap-32373
record_format	uketd_dc
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Spacecraft control Adaptive Neuro-Controller (ANC) Satellite Innovative Satellite (InnoSAT) Nano-satellite system
spellingShingle	Spacecraft control Adaptive Neuro-Controller (ANC) Satellite Innovative Satellite (InnoSAT) Nano-satellite system Norhayati, Mohd Nazid Adaptive neuro-controller design for nano-satellite attitude control
description	The motivation of this research is to bring the technology of spacecraft control into university education and to bring the possibility of developing our own satellite that will put us of equal standard with other developed nations. The purpose of this research is to develop the control scheme for three axes stabilization of nano-satellite system namely Innovative Satellite (InnoSAT). An adaptive neuro-controller (ANC) is applied as a controller in many application such as in robotics, power system, industries and etc. There are many successfully applications of ANC in controlling the satellite attitude control have been proposed. In this regards, four types of ANCs using two different control scheme and using two different algorithm for nano-satellite attitude control have been introduced in this research. These are ANC based on Model Reference Adaptive Control (MRAC) scheme trained by Back-Propagation (BP) algorithm, ANC based on MRAC scheme trained by Recursive Least Square (RLS) algorithm, ANC based on Internal Model Adaptive Control (IMAC) scheme trained by BP algorithm and ANC based on IMAC scheme trained by RLS algorithm. These two different control schemes are used by the ANC to adjust the output response of InnoSAT to follow the desired target. In this research, BP and RLS algorithms were used as an adjustment mechanism to update the parameters of the ANC. A multilayer perceptron (MLP) network with one hidden layer has the capability to approximate any continuous function up to certain accuracy. It is a very powerful technique in the discipline of control systems, especially when the controlled systems have large uncertainties and strong non- linearities. MLP network is used for ANC in this research. The design of ANC is initially started with design of ANC based on MRAC scheme using BP algorithm. Then, the ANC based on MRAC using RLS algorithm is designed and the performance for both ANCs based on MRAC were compared in term of convergence speed and possible divergence for certain conditions. The design is continued by designing the ANC based on IMAC scheme using BP algorithm and the last part of designing is designed the ANC based on IMAC scheme using RLS algorithm. The performance for both ANC based on IMAC scheme are also compared in term of convergence speed and possible divergence for certain conditions. The simulation results for all ANCs indicated that ANC using RLS algorithm have faster convergence speed compared to the ones trained by BP algorithm. The best ANC based on MRAC and ANC based on IMAC are compared with a conventional proportional, integral and derivative (PID) controller. Simulations have been carried out and for several reference inputs namely unit step, square wave and Y-Thompson. The simulation results are presented and the output responses show that the ANC based on MRAC performance is acceptable even in the case of the InnoSAT is subjected to varying gain, measurement noise, time delay and disturbance. Then, the ANC based on MRAC scheme is simulated with two axes cross coupling system and the simulation results show that the InnoSAT system is stable. The final simulation is tested the ANC with real time attitude reference which is Y-Thompson input reference. The results showed that the ANC based on MRAC scheme can stabilized the InnoSAT system even the system is subjected with varying gain, measurement noise, time delay and disturbance.
format	Thesis
author	Norhayati, Mohd Nazid
author_facet	Norhayati, Mohd Nazid
author_sort	Norhayati, Mohd Nazid
title	Adaptive neuro-controller design for nano-satellite attitude control
title_short	Adaptive neuro-controller design for nano-satellite attitude control
title_full	Adaptive neuro-controller design for nano-satellite attitude control
title_fullStr	Adaptive neuro-controller design for nano-satellite attitude control
title_full_unstemmed	Adaptive neuro-controller design for nano-satellite attitude control
title_sort	adaptive neuro-controller design for nano-satellite attitude control
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/2/Full%20text.pdf
_version_	1747836798866817024
spelling	my-unimap-323732014-03-05T08:33:18Z Adaptive neuro-controller design for nano-satellite attitude control Norhayati, Mohd Nazid The motivation of this research is to bring the technology of spacecraft control into university education and to bring the possibility of developing our own satellite that will put us of equal standard with other developed nations. The purpose of this research is to develop the control scheme for three axes stabilization of nano-satellite system namely Innovative Satellite (InnoSAT). An adaptive neuro-controller (ANC) is applied as a controller in many application such as in robotics, power system, industries and etc. There are many successfully applications of ANC in controlling the satellite attitude control have been proposed. In this regards, four types of ANCs using two different control scheme and using two different algorithm for nano-satellite attitude control have been introduced in this research. These are ANC based on Model Reference Adaptive Control (MRAC) scheme trained by Back-Propagation (BP) algorithm, ANC based on MRAC scheme trained by Recursive Least Square (RLS) algorithm, ANC based on Internal Model Adaptive Control (IMAC) scheme trained by BP algorithm and ANC based on IMAC scheme trained by RLS algorithm. These two different control schemes are used by the ANC to adjust the output response of InnoSAT to follow the desired target. In this research, BP and RLS algorithms were used as an adjustment mechanism to update the parameters of the ANC. A multilayer perceptron (MLP) network with one hidden layer has the capability to approximate any continuous function up to certain accuracy. It is a very powerful technique in the discipline of control systems, especially when the controlled systems have large uncertainties and strong non- linearities. MLP network is used for ANC in this research. The design of ANC is initially started with design of ANC based on MRAC scheme using BP algorithm. Then, the ANC based on MRAC using RLS algorithm is designed and the performance for both ANCs based on MRAC were compared in term of convergence speed and possible divergence for certain conditions. The design is continued by designing the ANC based on IMAC scheme using BP algorithm and the last part of designing is designed the ANC based on IMAC scheme using RLS algorithm. The performance for both ANC based on IMAC scheme are also compared in term of convergence speed and possible divergence for certain conditions. The simulation results for all ANCs indicated that ANC using RLS algorithm have faster convergence speed compared to the ones trained by BP algorithm. The best ANC based on MRAC and ANC based on IMAC are compared with a conventional proportional, integral and derivative (PID) controller. Simulations have been carried out and for several reference inputs namely unit step, square wave and Y-Thompson. The simulation results are presented and the output responses show that the ANC based on MRAC performance is acceptable even in the case of the InnoSAT is subjected to varying gain, measurement noise, time delay and disturbance. Then, the ANC based on MRAC scheme is simulated with two axes cross coupling system and the simulation results show that the InnoSAT system is stable. The final simulation is tested the ANC with real time attitude reference which is Y-Thompson input reference. The results showed that the ANC based on MRAC scheme can stabilized the InnoSAT system even the system is subjected with varying gain, measurement noise, time delay and disturbance. Universiti Malaysia Perlis (UniMAP) 2012 Thesis en http://dspace.unimap.edu.my:80/dspace/handle/123456789/32373 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/1/Page%201-24.pdf e24f70f7f98e4f3ced3fbd6ed54c87f6 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/2/Full%20text.pdf 72093b9b50592cf1f42278bcfcd89835 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/32373/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Spacecraft control Adaptive Neuro-Controller (ANC) Satellite Innovative Satellite (InnoSAT) Nano-satellite system School of Mechatronic Engineering

Adaptive neuro-controller design for nano-satellite attitude control

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