Welcome to the Real-Time Control Systems Leaderboard. This repository evaluates your MRAC NN control performance using RMSE (Root Mean Square Error) in Practice 2, Control PRedictivo e Inteligente - Universitat Politècnica de València.
The goal is to achieve the best tracking of the reference signal with your controller.
You can monitor the competition results and the latest submission plot here: 👉 [Competition Results]
- Tune your controller: Adjust your controler.
- Export your data: Save your test results into a
.csvfile.- Important: Your file must follow the standard export format (see below).
- Submit the Form: Upload your file and enter your name in the official submission form:
To ensure the automated judge can read your results, your CSV file must have the following structure:
| Column | Data Description |
|---|---|
| A | Row Index |
| B | Timestamp |
| C | Reference |
| D | Output |
Caution
Do not change the order of the columns. The evaluation script relies on these exact positions to calculate your score.
| Index, | Time, | Ref, | Output |
|---|---|---|---|
| 0, | 0.397, | 1300, | 0 |
| 1, | 0.417, | 1300, | 87 |
| 2, | 0.437, | 1300, | 523 |
| 3, | 0.457, | 1300, | 785 |
| ... | ... | ... | ... |
We use the Root Mean Square Error to rank your performance.
- A lower RMSE means better tracking and higher precision.
- The Grade is automatically calculated based on your RMSE (Max: 10.00).
- Python: Data processing and plotting (Pandas, Numpy, Matplotlib).
- GitHub Actions: Automated CI/CD pipeline for real-time assessment.
- Google Forms & Apps Script: Student submission bridge.
- Tailwind CSS: Live dashboard visualization.
Good luck and may the best controller win! 🤖🏎️