This repository hosts the code needed to reproduce the examples in the published work:
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C. Amo Alonso and N. Matni. Distributed and Localized Model Predictive Control via System Level Synthesis. Proceedings of the 59th IEEE Conference on Decision and Control. IEEE, 2020, pp. 5598-5605, doi: 10.1109/CDC42340.2020.9303936.
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C. Amo Alonso, J. Anderson, and N. Matni. Explicit Distributed and Localized Model Predictive Control via System Level Synthesis. Proceedings of the 59th IEEE Conference on Decision and Control. IEEE, 2020, pp. 5606-5613, doi: 10.1109/CDC42340.2020.9304349.
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C. Amo Alonso, J.S. Li, J. Anderson, and N. Matni. Robust Distributed and Localized Model Predictive Control via System Level Synthesis. arXiv Preprint, 2021, (https://arxiv.org/abs/2110.07010).
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C. Amo Alonso, J.S. Li, J. Anderson, and N. Matni. Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation. To appear in IEEE Transactions on Control of Network Systems, 2022, doi: 10.1109/TCNS.2022.3219770.
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C. Amo Alonso, J.S. Li, J. Anderson, and N. Matni. Distributed and Localized Model Predictive Control. Part II: Theoretical Guarantees. To appear in IEEE Transactions on Control of Network Systems, 2022, doi: 10.1109/TCNS.2023.3262650.
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C. Amo Alonso*, F. Yang*, and N. Matni. Data-Driven Distributed and Localized Model Predictive Control via System Level Synthesis. IEEE Open Journal of Control Systems, Vol. 1, pp. 29-40, 2022, doi: 10.1109/OJCSYS.2022.3171787.
*denotes equal contribution
This folder hosts the code needed to reproduce the examples in article [1] and its extended version https://arxiv.org/abs/1909.10074.
The names of the folders correspond to the figure's number that they generate. Since the experiments run for the extended version are more comprehensive, the figure numbers correpond to those in the extended version.
Each of the scnearios/cases shown in each of the figures is simulated individually, with a script named "scenario[number]" or "case[number]" as appropriate (open loop is named "scenario0"). To reproduce the figures in the paper, once the script is run the data must be saved in a .mat file named the same as the corresponding .m script. After all the scenarios in a folder have been simulated and the corresponding data have been run, "ploting_code.m" produces the figures shown in the paper.
Warning: some of the scripts, in particular the ones concerning runtime measures, might take several hours to run.
This folder hosts the code needed to reproduce the examples in article [2] and its extended version https://arxiv.org/abs/2005.13807.
The codes generate the data shown in Figure 2. Each of the scnearios/cases shown in each of the figures is simulated individually, with a script named "scenario[number]" or "case[number]" as appropriate (open loop is named "scenario0"). To reproduce the figures in the paper, once the script is run the data must be saved in a .mat file named the same as the corresponding .m script. After all the scenarios in a folder have been simulated and the corresponding data have been run, "ploting_code.m" produces the figures shown in the paper.
Warning: some of the scripts, in particular the ones concerning runtime measures, might take several hours to run.
This folder hosts the code needed to reproduce the examples in preprint [3].
This code requires the SLS-MATLAB toolbox by J. S. Li, available at https://github.com/sls-caltech/sls-code.
Download this repository and make sure the /matlab directory and its subfolders are included in your MATLAB path. This can be done by opening MATLAB from the sls-code/matlab directory and running the init.m script located in that directory. Then, add the desired scripts to your MATLAB path by navigating to the appropriate folder (2021_arXiv_Robust-DLMPC) in MATLAB and running the init.m script in the this folder. After these steps, the desired scripts can be run normally. The are located in the corresponding folder named after the figure/table that they generate.
Note: the functions containing the algorithms described in preprint [3] are implemented within the SLS-MATLAB toolbox. Interested users might refer to the specific functions located in the SLS-MATLAB toolbox repository.
Warning: some of the scripts, in particular the ones concerning runtime measures, might take several hours to run.
This folder hosts the code needed to reproduce the examples in article [4].
This code requires the SLS-MATLAB toolbox by J. S. Li, available at https://github.com/sls-caltech/sls-code.
Download this repository and make sure the /matlab directory and its subfolders are included in your MATLAB path. This can be done by opening MATLAB from the sls-code/matlab directory and running the init.m script located in that directory. Then, add the desired scripts to your MATLAB path by navigating to the appropriate folder ( 2022_TNCS_DLMPC-Part-I) in MATLAB and running the init.m script in the this folder. After these steps, the desired scripts can be run normally. The are located in the corresponding folder named after the figure/table that they generate.
Note: the functions containing the algorithms described in article [4] are implemented within the SLS-MATLAB toolbox. Interested users might refer to the specific functions located in the SLS-MATLAB toolbox repository.
Warning: some of the scripts, in particular the ones concerning runtime measures, might take several hours to run.
This folder hosts the code needed to reproduce the examples in article [5].
This code requires the SLS-MATLAB toolbox by J. S. Li, available at https://github.com/sls-caltech/sls-code.
Download this repository and make sure the /matlab directory and its subfolders are included in your MATLAB path. This can be done by opening MATLAB from the sls-code/matlab directory and running the init.m script located in that directory. Then, add the desired scripts to your MATLAB path by navigating to the appropriate folder ( 2022_TNCS_DLMPC-Part-II) in MATLAB and running the init.m script in the this folder. After these steps, the desired scripts can be run normally. The are located in the corresponding folder named after the figure/table that they generate.
Note: the functions containing the algorithms described in article [5] are implemented within the SLS-MATLAB toolbox. Interested users might refer to the specific functions located in the SLS-MATLAB toolbox repository.
Warning: some of the scripts, in particular the ones concerning runtime measures, might take several hours to run.
This folder hosts the code needed to reproduce the examples in article [6].
The names of the subfolders correspond to the figure's number that they generate. Users must first run the script named script_[corresponging figure].m, which will save the data in folder named results as a .mat file. Once this is done, users must run the script named plot_[corresponging figure].m, located in the same folder where the first script was run. This will produce the desired figure.
Note: To run the script, users must change the current directory to the one the script is in.
Warning: some of the scripts, in particular the ones concerning runtime measures, might take several hours to run.