Abstract
The current research focuses on the designing of an intelligent controller for the Attitude Control System (ACS) of the Innovative Satellite (InnoSAT). The InnoSAT mission is to demonstrate local innovative space technology amongst the institutions of higher learning in the space sector. In this study, an Adaptive Neuro-controller (ANC) based on the Hybrid Multi Layered Perceptron (HMLP) network has been developed. The Model Reference Adaptive Control (MRAC) system is used as a control scheme to control a time varying systems where the performance specifications are given in terms of a reference model. The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output. The objective of this paper is to analyse the time response and the tracking performance of the ANC based on the HMLP network and the ANC based on the standard MLP network for controlling an InnoSAT attitude. These controllers have been tested using an InnoSAT model with some variations in operating conditions such as varying gain, measurement noise and disturbance torques. The simulation results indicated that the the ANC based on the HMLP network is adequate to control satellite attitude and give better results than the ANC based on the MLP network.
Highlights
Small satellites have become more popular in the last few decades due to their relative simplicity resulting in an attractive short period of design and in low cost (Bushenkov, 2002; Martinelli & Pena, 2005)
The results show that the Adaptive Neuro-controller (ANC) based on the Hybrid Multi Layered Perceptron (HMLP) network have given significant improvement in the performance of controlling unstable systems (Sharun, Mashor, Jaafar, Yaacob & Norhayati, 2010a; Sharun et al, 2010b & Sharun et al, 2010c)
Based on the above analyses, the simulation results of the ANC based on the HMLP network and the ANC based on the standard MLP network are compared for satellite attitude control of the Innovative Satellite (InnoSAT) model
Summary
Small satellites have become more popular in the last few decades due to their relative simplicity resulting in an attractive short period of design and in low cost (Bushenkov, 2002; Martinelli & Pena, 2005). The usual ACS used in small or large satellites includes several kinds of sensors, actuators and an on-board computer that processes the data through a control algorithm (Martinelli & Pena, 2005). The development of an intelligent real time control system based on neural network is possible for the satellite in space that has been exposed to non-probabilistic uncertainties such as sun flare and time-dependant noises in measurement (Zak, 2003). The results show that the ANC based on the HMLP network have given significant improvement in the performance of controlling unstable systems (Sharun, Mashor, Jaafar, Yaacob & Norhayati, 2010a; Sharun et al, 2010b & Sharun et al, 2010c). IK( INPy t(n(e(t Kett)r)u)1tc( h)rt eo)i Pp s jn_ t(hK1TCrt(wrot (e(o 2ntjts1n)F)1e)et )P( ar(H iFo(r t(n tcit1lM) Klhwv e 1(,(i1Lrj1tt)(v))t)tP(i )0h/AyI) (e (t Nt)1n() cteC )otTb e)wn11P.j Tt ovA(r((to rtt)k n)li n 1 ei0th)u(wo tarril( saqvtl)1i0ub)n( eet1e)4etwn8wiolslreklbececatelgldeednaestrhaettheHe((d11y12bf(b))1a(r((r9670osi))))idms MftohurelttihA-LedaaAypeNtriveCde, P(t) I K (t) T (t) P(t 1) / (t)
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