| dc.description.abstract | 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. | en_US |
