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Aditya Pulipaka
2026-03-20 22:43:07 -05:00
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@@ -90,11 +90,14 @@ The environment is controller-agnostic — any control algorithm can be plugged
| File | Controller | | File | Controller |
|------|-----------| |------|-----------|
| `pid_simulation.py` | Feedforward LUT + PID |
| `lev_PID.ipynb` | Interactive PID tuning notebook | | `lev_PID.ipynb` | Interactive PID tuning notebook |
| `optuna_pid_tune.py` | Optuna-based automated PID hyperparameter search |
| `lev_PPO.ipynb` | PPO reinforcement learning via Stable-Baselines3 | | `lev_PPO.ipynb` | PPO reinforcement learning via Stable-Baselines3 |
### Supplementary Files for PID:
`pid_simulation.py`: Feedforward LUT + PID
`optuna_pid_tune.py`: Optuna-based automated PID hyperparameter search
Pre-tuned PID gain sets are saved in `pid_best_params*.json` (variants for 1500, 3000, and 6000 Optuna trials). Pre-tuned PID gain sets are saved in `pid_best_params*.json` (variants for 1500, 3000, and 6000 Optuna trials).
### PWM circuit model ### PWM circuit model