Nonlinear Model Predictive Controller
Implementation on a Microprocessor
for 2D Lunar Powered Descent
Author(s)
Sharma, Saumya
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Abstract
Designing the optimal lunar landing trajectory for a single thrust output, SmallSat form factor vehicle involves analyzing the trajectory from a deorbit burn (braking) and powered
descent phase. Due to the need to reduce altitude and velocity while optimizing for maximum
landing mass, the system is modeled with non-linear dynamics based on various assumptions
which are described in the paper. Open-loop continuous propagation is used with optimal
control theory to establish a trajectory that will be used as the source of truth and compared
against the hardware implementation of this simulation. Because the designed control
algorithm needs to be capable of flying on an on-board computer, a model predictive
controller (MPC) was implemented to show how discrete real-time updates impact the
optimization of the trajectory. MPC reduces the computational load through an online
optimization algorithm instead of using a true optimization problem to produce a more flyable
control scheme. To show the effects of running an MPC for a “flight-like” algorithm on a
processor that would fit in a SmallSat form-factor, a Raspberry Pi 3B was used to demonstrate
how varying the time horizon length and time-step frequency impact computing performance
and fuel consumption
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Date
2023-05-01
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Text
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Masters Project
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