INNOSAT ATTITUDE CONTROL SYSTEM BASED ON ADAPTIVE NEURO-CONTROLLER

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.