This repository contains embedded firmware, sensor characterization tooling, and a physics-based simulation stack as well as control algorithm trials for the Texas Guadaloop maglev system.
Arduino firmware for reading Baumer inductive analog distance sensors. Uses interrupt-driven ADC sampling at ~77 kHz (16 MHz / prescaler 16) for low-latency gap measurement. Tracks the 10 lowest and 10 highest in-range ADC values over a sampling window to help establish calibration bounds. An out-of-range (OOR) digital pin is monitored in the ISR to discard invalid readings.
Key details:
- ADC ISR at ~77 kHz; readings discarded automatically when OOR pin is HIGH
- Serial commands: `1` to start sampling, `0` to stop and print boundary statistics
- Baud rate: 2,000,000
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## Sensor Characterization
### `sensor/`
Python pipeline for converting raw ADC readings from the inductive gap sensors into calibrated millimeter distances.
- **`sensorCollector.py`** — Serial interface that reads live ADC values from Arduino and applies the calibration model in real time.
- **`analogFitter-*.py`** — Curve-fitting scripts (polynomial, exponential, 3/4/5-parameter logistic) that fit calibration sweep data (`data*.csv`) to find the best sensor model. The 5-parameter generalized logistic form was found to give the best fit.
- **`Sensor*Averages.csv` / `data*.csv`** — Raw and averaged calibration data for sensors 0–3. Sensor 3 required a different voltage divider (20 kΩ / 50 kΩ) because the induction sensor output exceeds 6 V.
Calibration constants (A, K, B, C, v) for the generalized logistic model are embedded directly in `sensorCollector.py` and `sensor_simplerNew.py` for deployment.
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## Magnetic Characterization (`TwoCellMagChar/`)
A minimal bench rig used to validate Ansys Maxwell FEA force predictions experimentally.
- **`TwoCellMagChar.ino`** — Drives two H-bridge coil channels across a sweep of PWM values (−250 to +250 in steps of 50) while reading two HX711 load cells simultaneously. Averages 10 measurements per PWM step and reports gram-force readings over serial.
- **`MagCharTrial.xlsx`** — Recorded force-vs-PWM data from physical trials.
- **`CalibConsts.hpp`** — Load-cell offset and scale constants shared with `loadCellCode/`.
The measured force curves confirmed that the Ansys sweep data is in reasonable agreement with physical hardware, providing confidence for using the FEA model inside the simulation.
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## Levitation Simulation (`lev_sim/`)
A PyBullet-based simulation environment for 4-point active magnetic levitation of the pod. The simulation is driven by an Ansys Maxwell parametric sweep (coil currents × gap height × roll angle → force & torque), fitted to a polynomial regression model for fast inference.
### Data pipeline
1.**Ansys sweep** — `Ansys Results 12-9.csv / .xlsx` contains FEA results sweeping left/right coil currents, roll angle, and gap height.
2.**Function Fitting** — `Function Fitting.ipynb` fits the Ansys data to a `PolynomialFeatures + LinearRegression` model (inputs: `currL`, `currR`, `roll`, `1/gap`). The trained model is saved to `maglev_model.pkl`.
3.**Fast inference** — `maglev_predictor.py` (`MaglevPredictor`) loads the pickle and bypasses sklearn overhead by extracting raw weight matrices, running pure-NumPy polynomial expansion for ~100× faster per-sample prediction.
### Simulation environment
`lev_pod_env.py` implements a [Gymnasium](https://gymnasium.farama.org/) `Env` wrapping PyBullet:
- **State**: 4 gap heights (normalized) + 4 gap-height velocities
