الفهرس 
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Abstract
This thesis analyzes and develops the methods of accurate satellite orbits determination and estimation using adaptive Least N lean Squares (LNIS), Kalman dicital Olters, and Adaptive Neural Networks (ANN). In addition, the thesis develops and implements satellite ot bits controllers using an Adaptis e Neural Network Predictive Control (ANNP() technique. After a spacecraft has been placed in an operational orbit about the barth, it will lose its orbit due to physical perturbation lorces bum the I arth. the Sun, and the N loon. Subsequent thrusting maneu ers ill be required to correct the satellite orbit. Orbit estimation and determination are required with high accuracy, hich are the Orsi and the most important phase in preparation of the satel lite-control-maneu\ ci. [he thesis speci l)cally addresses the algorithmic implementation of the USIS and Kalman I) hers for the spacecraft orbit determination and estimation Problem. 1he inherent advantages, disad\ antages and trade-ofI which are required to select the suitable method, have been analv/ed. ‘I he orbit determination and estimation has been relined using multi-layers ANN with a non-linear function. The efflciency, perlc)rmance, accuracy, running time, and the inherent characteristics of the three difherent approaches have been analyzed. The ANN has sho\\ n a good agreement with the definitive reference solution of the Nilsat- 101 geostationar operating satellite. An adequate mathematical satellite simulation model is implemented by treating the orbital elements as the dependent variables of a set ot first order dillerential equations using numerical integration methods to simulate the satellite orbit trajectory during thrusting maneuver. The thesis develops an ANNPC to control satellite orbits using the implemented Neural Net\\ ork Predictive (‘unit ol block-set of the N IA t.At3 package. When using ANN for control, t\\ o steps, S stein ILlenti limit ion and Control I )esign. are used. In The s stem identification stage .an ANN model is de\ eloped to represent the forward d namics of the
satellite. ‘[he ANN model is trained using the implemented simulation model. The prediction error bet\\een the implemented satellite model output and the ANN output is used as the ANN training signal. In the ss stem control stage, the AN N model is used to predict future satellite responses to potential control signals. Using ANNPC in orbit control ‘ ill optimize the thrust threes and satellite parameters due to its inherent characteristic. AN N PC permits on-board maneuver planning. calc ulation. introduces safety condition in case of Earth Control Stations (ECS) unavailabilit , and creates added s stem robustness. ANN1C v ill be efficient in the autonomous satellite generations and can change the way space segment and missions operate.