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

• URL: http://arxiv.org/abs/2106.00007v1
• Date: Sun, 30 May 2021 23:35:13 GMT
• Title: DikpolaSat Mission: Improvement of Space Flight Performance and Optimal Control Using Trained Deep Neural Network -- Trajectory Controller for Space Objects Collision Avoidance
• Authors: Manuel Ntumba, Saurabh Gore, Jean Baptiste Awanyo
• Abstract summary: This paper shows how the controller demonstration is carried out by having the spacecraft follow a desired path. The obstacle avoidance algorithm is built into the control features to respond spontaneously using inputs from the neural network. Multiple algorithms for optimizing flight controls and fuel consumption can be implemented using knowledge of flight dynamics in trajectory.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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