Code for our paper Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty at CDC 2022.
We implemented three different algorithms: two tubes, single tube and the proposed SM MPC. Detailed descriptions of each method can be found in the paper.
Tested using Python 3.7 and CasADi 3.5
The files containing "cstr" only generate the constraint tightening, while the files without "cstr" generate the closed loop trajectories. "qr" in the file name means quadrotor. SSE.py is the implementation of set-membership state estimation. More detailed comments can be found in the code.
The results folder contains all necessary data for the results presented in the paper (i.e., Figure 2, 3, and 4).
If you find the code useful, please consider citing our paper:
@inproceedings{ji2022robust,
title={Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty},
author={Ji, Tianchen and Geng, Junyi and Driggs-Campbell, Katherine},
booktitle={2022 IEEE 61st Conference on Decision and Control (CDC)},
pages={1782--1789},
year={2022},
organization={IEEE}
}
Feel free to reach me at tj12@illinois.edu if you have any questions.