Use Cases ========= .. raw:: html The use cases of c4dynamics span different systems: a quadcopter, a missile, a ballistic object, a car, and a laboratory helicopter. They run different algorithms: PID and model predictive control, Kalman filters, proportional navigation, and neural network control. Yet they are all built the same way. **One representation** Every system is described in state space. A state vector :math:`X` holds the variables that define the system at a given moment, the equations of motion describe how it evolves, :math:`\dot{X} = f(X, u)`, and the sensors measure a function of it, :math:`z = h(X)`. In c4dynamics, the state vector is an object. A :class:`state ` object, or one of its library forms (:class:`datapoint `, :class:`rigidbody `, :class:`pixelpoint `), has the same interface for any system: :attr:`X ` to get and set the state vector, :meth:`store ` and :meth:`data ` to record and retrieve its history, and :meth:`plot ` to view it. **One loop** Each use case follows the same loop: 1. **State**: define the state object of the system. 2. **Dynamics**: propagate the state by the equations of motion. 3. **Algorithm**: estimate the state from the measurements, and compute the control command. 4. **Action**: apply the command, store the state, and repeat. The structure repeats. Only the physics changes. **The switch** Because the interface stays the same, moving from one system to another means replacing the physics: the state variables and the equations of motion. The algorithm, the simulation loop, the storage, and the plots stay intact. The use cases show this in both directions. The same quadcopter model is flown by a cascade PID, estimated by an extended Kalman filter, and controlled by the ArduPilot flight controller. The same :class:`ekf ` object estimates the full state of a quadcopter and the ballistic coefficient of a falling object, and its linear sibling, :class:`kalman `, tracks a car in a video. Pick a use case below, and look for what stays the same. .. raw:: html

Cascade PID

Quadcopter figure-8 trajectory tracking

Quadcopter · Control

EKF State Estimation

Quadcopter GPS/IMU/MAG sensor fusion

Quadcopter · Estimation

ArduPilot Simulation

Quadcopter figure-8 under ArduCopter control

Quadcopter · Control · Software in the loop

6-DOF

Proportional navigation guidance

Missile · Guidance

Extended Kalman Filter

Ballistic coefficient estimation

Ballistic object · Estimation

Model Predictive Control

Vehicle steering

Car · Control

YOLO3 Detector & Kalman Filter

Car tracker

Car · Computer vision · Estimation

YOLO11 Detector & Kalman Filter

Car tracker, modern detector refresh

Car · Computer vision · Estimation

Neural Network Based Controller

Helicopter learning control

Helicopter · Control · Machine learning
.. toctree:: :hidden: :maxdepth: 0 ekf_estimation/quad_ekf.ipynb ardupilot_sim/ardupilot_fig8.ipynb ballistic_ekf/ballistic_coefficient.ipynb mpc_steering/mpc_steering.ipynb car_tracker/car_tracker.ipynb car_tracker_yolo11/car_tracker_yolo11.ipynb