From vehicle data to a firmware experiment

flowchart TD
    data["Vehicle data<br/>Measurements, CAD, datasheets"] --> choice{"Suitable library model?"}
    choice -->|Yes| library["Configure a library template"]
    choice -->|No| physics["Write or extend the physics"]
    library --> model["Modelica vehicle model"]
    physics --> model
    model --> simulate["Compile and simulate with FastDyn"]
    firmware["Firmware + board configuration"] --> simulate
    simulate --> tune["Tune gains with repeatable maneuvers"]
    seeds["Gains from a similar vehicle"] --> tune
    tune --> validate["Validate trajectory and robustness"]
    validate -->|Revise assumptions or gains| model
    validate --> results["Save models, TOML, logs, and plots"]

1. Gather the data

Measure the flying mass with its battery and payload. Here, CG means center of gravity. Obtain motor locations from drawings or measurements, and estimate inertia from CAD or component masses and their positions. Use motor/propeller thrust measurements for the actuator model. Record units, reference frames, test conditions, and uncertain quantities with the model.

2. Choose a nearby model or a template

Search third_party/common/modelica_models for a plant with appropriate states and forces. The tutorial reuses Vehicles.Templates.QuadrotorPlant and wraps it with FastDyn.Copter to expose the sensor/PWM interface. Use extends to make a named vehicle variant, or change the equations when the existing physics cannot represent the behavior you need.

The library also contains named vehicles and closed-loop controller/mission models. See model library and roadmap for those starting points and how Modelica controller ports complement firmware runs in FastDyn.

3. Pair the plant with firmware

The model defines the vehicle. The TOML also chooses the firmware binary, board configuration, sensor drivers, timing, helper processes, and controller parameter file. Compile with Rumoca, then run the resulting FMI 3.0 plant with ArduCopter 4.6.2 in FastDyn. Verify stationary sensors and actuator directions before evaluating a flight.

4. Tune, then validate

Start with conservative gains or a documented similar-frame seed. Use a repeatable maneuver to compare tracking, oscillation, altitude retention, and motor limits. Keep the selected gains fixed for a separate mission and an uncertainty study. A tune that fits one idealized plant may have little margin when inertia or motor dynamics change.

Why equations matter

A TOML or SDF parameter set selects numbers for behavior implemented elsewhere. With Modelica, you can also express equations, component connections, and additional states, then compile those into the plant. In the load exercise, the model rotates an applied force into body coordinates and computes its moment from the attachment location. The plant adds both to its equations of motion.

Gazebo/SDF can also represent offset inertias, joints, and payloads, and plugins can add custom forces. The advantage illustrated here is keeping the physical relationships in composable Modelica source with the vehicle, while retaining FastDyn’s firmware-driver interface.

Next, read how FMI connects the compiled plant to FastDyn, then run the baseline mission.