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This paper introduced the space mission DikpolaSat Mission, how this researchfits into the mission, and the importance of having a trained DNN model insteadof the usual GN&C functionality. This paper shows how the controllerdemonstration is carried out by having the spacecraft follow a desired path,specified in the referenced model. Increases can be made by examining the routeused to construct a DNN and understanding the effects of various activatingfunctions on system efficiency. The obstacle avoidance algorithm is built intothe control features to respond spontaneously using inputs from the neuralnetwork for collision avoidance while optimizing the modified trajectory. Theaction of a neural network to control the adaptive nature of the nonlinearmechanisms in the controller will make the control system capable of handlingmultiple nonlinear events and also uncertainties that have not been induced inthe control algorithm. Multiple algorithms for optimizing flight controls andfuel consumption can be implemented using knowledge of flight dynamics intrajectory and also in the event of obstacle avoidance. This paper alsoexplains how a DNN can learn to control the flight path and make the systemmore reliable with each launch, thereby improving the chances of predictingcollisions of space objects. The data released from this research is used todesign more advanced DNN model capable of predicting other orbital events aswell.

DikpolaSat Mission: Improvement of Space Flight Performance and Optimal  Control Using Trained Deep Neural Network -- Trajectory Controller for Space  Objects Collision Avoidance