Performance of a Model Predictive Control Based Autonomous Rendezvous and Docking Algorithm for CubeSats using Hardware Emulation

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Performance_of_MPC_Based_AR&D_Algorithm_for_CubeSats_Using_Hardware_Emulation.pdf (456.31 KB)
Author(s)
Fear, Andrew J.
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Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
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https://hdl.handle.net/1853/74028
Abstract
Hardware emulation of typical CubeSat flight computers is utilized to benchmark the performance of a three-phase Model Predictive Control (MPC) algorithm for autonomous rendezvous and docking (AR&D). The length of the MPC prediction horizons affects the computational complexity and therefore the solution time of finding an optimal control sequence. This study investigates the limitations, if any, of current state-of-the-art CubeSat flight systems regarding the ability to take advantage of this type of guidance algorithm. A virtual machine with an ARM processor typical of CubeSat available hardware is used to test the performance of the algorithm. Monte Carlo simulations are run to calculate the average computation time per optimal control solution and compare these values across varying prediction horizon lengths.
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2023-02
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