This repository showcases the trajectory optimization code for the Europa Lander and Mapping Orbiter (ELMO) mission. The project is part of Imperial College London's Group Design Project in the 3rd year.
The code implements optimal trajectory generation for the landing phase of the ELMO mission, utilizing control theory and "Lossless Convexification" method to minimize fuel consumption during descent.
During the de-orbit, descent, and landing phase, the trajectory is split into two main phases:
- Powered descent trajectory optimization.
- Simulation of landing dynamics on Europa's surface.
The codebase includes the following main files:
main.m: The main script for running the trajectory optimization and analysis.pre_descent_stage.m: Function to compute optimal trajectory for the powered descent stage.solve_pdg_fft_descent.m: Function to solve the optimal control problem for the powered descent.pre_lander.m: Function to compute optimal trajectory for the landing stage.solve_pdg_fft_lander.m: Function to solve the optimal control problem for the landing.- Additional utility functions for plotting and analysis.
To run the code, you will need:
- MATLAB with support for the CVX optimization package. Please visit the CVX website for installation instructions.
- Ensure all required files are in your MATLAB path.
- Open
main.mand run the script to execute the trajectory optimization.
This project is part of the ELMO mission, aiming to explore Europa's surface and gather crucial data for future missions. The trajectory optimization code is a key component in ensuring a successful landing.
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Acikmese, B., & Ploen, S. R. (2007). Convex Programming Approach to Powered Descent Guidance for Mars Landing. Journal of Guidance, Control, and Dynamics, 30(5), 1353-1366. Available from: https://doi.org/10.2514/1.27553.
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Malyuta, D., Reynolds, T. P., Szmuk, M., Lew, T., Bonalli, R., Pavone, M., et al. (2022). Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently. IEEE Control Systems, 42(5), 40-113. Free preprint available at https://arxiv.org/abs/2106.09125. Available from: https://doi.org/10.1109/mcs.2022.3187542.
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Blackmore, L., A¸cikme¸se, B., & Scharf, D. P. (2010). Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization. Journal of Guidance, Control, and Dynamics, 33(4), 1161-1171. Available from: https://doi.org/10.2514/1.47202.
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A¸cıkme¸se, B., Carson, J. M., & Blackmore, L. (2013). Lossless Convexification of Nonconvex Control Bound and Pointing Constraints of the Soft Landing Optimal Control Problem. IEEE Transactions on Control Systems Technology, 21(6), 2104-2113.
Feel free to explore the code and adapt it for your own analyses!