ILQR and MPC Swarm Control using Random Finite Set Theory Applied to the Clohessy-Wiltshire Relative Motion Model and Acceleration Model.
MATLAB Code by Bryce Doerr doerr024(at)umn.edu
Algorithm used in the paper "Random Finite Set Theory and Optimal Control for Large Spacecraft Swarms" by: Bryce Doerr, Richard Linares, Pingping Zhu, Silvia Ferrari https://arxiv.org/abs/1810.00696
See the README for each implmentation
The GIF below shows a visualization of the RFS Control for the Clohessy-Wiltshire Relative Motion Problem. The swarm contains 77 agents that move into a star formation.
Other papers used: ILQR algorithm is based on Yuval Tassa's implementation https://www.mathworks.com/matlabcentral/fileexchange/52069-ilqg-ddp-trajectory-optimization Tassa, Yuval, Tom Erez, and Emanuel Todorov. "Synthesis and stabilization of complex behaviors through online trajectory optimization." Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE, 2012.
RFS Formulation based on Ba-Ngu Vo Vo, Ba-Ngu, and Wing-Kin Ma. "The Gaussian mixture probability hypothesis density filter." IEEE Transactions on signal processing 54.11 (2006): 4091.
Other Code used: -GIF Plotting by Nicolae CINDEA from https://www.mathworks.com/matlabcentral/fileexchange/17463-movie-to-gif-converter?s_tid=prof_contriblnk -Adapted code from Polygon point Plotting by Sulimon Sattari from https://ww2.mathworks.cn/matlabcentral/fileexchange/41454-grid-of-points-within-a-polygon
Copyright 2018 Bryce Doerr
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