  This paper introduced the space mission DikpolaSat Mission, how this research
fits into the mission, and the importance of having a trained DNN model instead
of the usual GN&C functionality. This paper shows how the controller
demonstration is carried out by having the spacecraft follow a desired path,
specified in the referenced model. Increases can be made by examining the route
used to construct a DNN and understanding the effects of various activating
functions on system efficiency. The obstacle avoidance algorithm is built into
the control features to respond spontaneously using inputs from the neural
network for collision avoidance while optimizing the modified trajectory. The
action of a neural network to control the adaptive nature of the nonlinear
mechanisms in the controller will make the control system capable of handling
multiple nonlinear events and also uncertainties that have not been induced in
the control algorithm. Multiple algorithms for optimizing flight controls and
fuel consumption can be implemented using knowledge of flight dynamics in
trajectory and also in the event of obstacle avoidance. This paper also
explains how a DNN can learn to control the flight path and make the system
more reliable with each launch, thereby improving the chances of predicting
collisions of space objects. The data released from this research is used to
design more advanced DNN model capable of predicting other orbital events as
well.
