闭环数据启用预测控制及其与闭环子空间预测控制的等效性matlab代码.zip

# CL-DeePC and its equivalence with CL-SPC This repository contains the necessary components of the Automatica article titled "Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control". N.B.: Supplied MATLAB classes are still subject to further development. Classes are currently only meant to function with the implemented settings, as supplied. ## Project organization - PG = project-generated - HW = human-writable - RO = read only ``` . ├── .gitignore ├── CITATION.md ├── LICENSE.md ├── README.md ├── bin <- Compiled and external code, ignored by git (PG) │ ├── external/Casadi <- When running simulations on a cluster, place compiled CasADi code with IPOPT solver here. (RO) | └── model_Favoreel1999.m ├── data <- All project data, ignored by git │ ├── processed <- The final, canonical data sets for modeling. (PG) │ ├── raw <- The original, immutable data dump. (RO) │ └── temp <- Intermediate data that has been transformed. (PG) ├── results │ └── figures <- Figures for the manuscript or reports (PG) └── src <- Source code for this project (HW) ``` ## Configuration & use: The generated data was obtained by varying either one of three parameters: - Nbar: number of samples used - p=f: window sizes - Re: innovation noise variance The functions to generate the relevant data and a sample script for submission of multiple jobs to a Slurm cluster can be found in the corresponding directories src/d_{Nbar,pf,Re}. The data that is used in the article can be obtained in one of two ways: 1. Download it from [](https://doi.org/10.5281/zenodo.10573875) and place it in the data\raw directory. 2. Generate the data from this GitHub repository: [](https://zenodo.org/doi/10.5281/zenodo.10573259). To do this follow the below steps:\ Step 1: Generate the the necessary data using the Slurm submission script (or make a similar Matlab script) to run different simulation cases with the functions 'varying_{...}.m' found in the relevant aformentioned directory.\ Step 2: The generated data should automatically be placed in a descriptively named folder in the data/raw directory. Go to this new folder with the new data and run 'src/dirs2res.m'. To generate the figures in the paper run the script 'src/make_figures.m'. The figures will be placed inside the results/figures directory. ## License This project is licensed under the terms of the [MIT License](/LICENSE.md)