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An Attitude control system for a satellite in low earth orbit is designed in this project. As a reference, CARTOSAT-2 is selected to design the AOCS. CARTOSAT-2 is located in LEO so it suits the needs of the projects. It is mainly used for mapping of urban, rural areas and wetlands in India. To design a basic AOCS, a DC motor based model is considered. Transfer functions of DC motor and satellite dynamics model are calculated using Laplace transformation. Stability of the system is checked by plotting poles and zeroes of the systems. PID controller is used to improve the overall stability of the system by decreasing the errors in the output of the system. The systems are subjected to sine and step inputs and responses are plotted in the form of graphs. The plots are studied using SciLab as the tool to design the block diagram and the control system for the AOCS. Scilab along with FlightGear is used to visualize the response of the system. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of adaptive neural networks through simulations for satellite systems involving three-axis attitude control algorithms. PID tuning is the method employed traditionally. An optimally tuned, to minimizes the deviation from set point. It also responds quickly to the disturbances with some minimal overshoot. However, the disadvantage of poor performance has been observed in these controllers when manual tuning is used which in itself a monotonous process is. The PID controller using Ziegler-Nichols has more transient responses of satellite such as Overshoot, Settling time, and Steady state errors. For overcome this technique, the proposed analysis implemented an Adaptive Neural Network with PID tuning. The paper aims to combine two feedback methods by using neural networks. These methods are feed-forward and error feedback adaptive control. The research work is expected to reveal the inside working of these neural network controllers for state and error feedback input states. An error driven adaptive control systems is produced, when the neural networks acquire the knowledge of slopes and gains regarding the error feedback, while, with state feedback the system will keep trying to approximate a stable approach in order to stabilize the attitude of the satellite. [ABSTRACT FROM AUTHOR]