Code and dataset to analyze output-constrained IPC methods ℓ2-IPC and ℓ∞-IPC

This code and dataset were used to analyze the ℓ2 and ℓ∞ output-constrained individual pitch control methods. We have proposed these methods and presented these results in our corresponding publication: Output-constrained individual pitch control methods using the multiblade coordinate transformation: Trading off actuation effort and blade fatigue load reduction for wind turbines &lt;insert once available as preprint/published&gt;.<br>The code can also be found on GitHub. However, the GitHub repository will be updated for potential future publications, whereas this version of the code will remain unaltered in this repository.<br>The code can be used to run OpenFAST simulations of the ℓ2 and ℓ∞-IPC control methods and analyze the results. You can also recreate our results by downloading the results files (`data - laminar results.zip` and `data - turbulent results.zip`) and unzipping them into a `Results/data` folder in the same root folder as the analysis code.<br>A good starting point to run simulations is to first generate input files with `src/generate_cases.py` after which you can run these cases with `src/Run_cIPC.m`. To analyze the results (either self-generated or downloaded from this page), use or take inspiration from `Plot_WES.m`, which we used to generate the results for the corresponding publication. For easier plotting, we've used Jesse's `preplot-postplot` Matlab functions, which can also be found on GitHub. For more information see the `README.md`.

本代码与数据集用于分析基于ℓ₂与ℓ_∞输出约束的独立变桨控制（Individual Pitch Control, IPC）方法。我们提出了上述方法，并将相关研究成果发表于对应学术论文：《基于多桨叶坐标变换的输出约束独立变桨控制方法：权衡风力涡轮机的驱动代价与叶片疲劳载荷削减效果》<待预印本或正式出版后插入>。
本代码同样可在GitHub平台获取。不过该GitHub代码仓库将针对未来潜在的研究成果进行更新，而本仓库中的此代码版本将保持不变。
本代码可用于运行ℓ₂与ℓ_∞-IPC控制方法的OpenFAST仿真并分析结果。您也可通过下载结果文件（`data - laminar results.zip`与`data - turbulent results.zip`），并将其解压至与分析代码同根目录下的`Results/data`文件夹中，以此复现本文的研究结果。
开展仿真的便捷入门流程为：首先通过`src/generate_cases.py`生成输入文件，随后使用`src/Run_cIPC.m`运行这些仿真案例。若要分析仿真结果（无论是自行生成还是从本页面下载的结果），可参考`Plot_WES.m`的实现逻辑——该脚本正是我们用于生成对应学术论文配图的工具。为简化绘图流程，我们使用了Jesse开发的`preplot-postplot` Matlab工具函数，该工具同样可在GitHub平台获取。更多细节请参阅`README.md`文件。