Drone_Python_MPC

This project implements Nonlinear Model Predictive Control (NMPC) for drones using Python. The goal of this project is to implement trajectory tracking and control for drones using Model Predictive Control (MPC) algorithms. The project includes implementations of various drone models.

gazebo.mp4

Integrating NMPC with a low-level PID controller in a comprehensive simulation environment using Gazebo, PX4, and ROS.

1. Installation

1. Install acados and its dependencies, refer to the acados official documentation.

2. Clone this repository:

   git clone https://github.com/KafuuChikai/Drone_Python_MPC.git
   cd Drone_Python_MPC
   

2. File Structure

drone_model/                : Directory containing different drone models.
  ├── drone_simulator.py    : model test.
  ├── drone_model.py        : Full drone model.
  ├── drone_model_s.py      : Simple drone model.
  ├── drone_model_s_drag.py : Drone model with drag force.
  ├── drone_model_s_drag_delay.py : Drone model with drag force and delay.
data/                       : Directory for storing generated data.
  ├── drone_track.csv       : CSV file containing drone trajectory data.
  ├── drone_state.csv       : CSV file containing drone state data.
  ├── drone_control.csv     : CSV file containing drone control data.
acados_models/              : Directory for storing generated acados model files.
c_generated_code/           : Directory for storing generated c code.
[drone_opt_track.py]
[drone_opt_simple_point.py]
[drone_opt_simple_track.py]
[drone_opt_s_drag_track.py]
[drone_opt_s_drag_delay_track.py]

• drone_opt_track.py: Full model drone trajectory tracking.
• drone_opt_simple_point.py: Simple drone optimization to reach a point.
• drone_opt_simple_track.py: Simple drone trajectory tracking optimization.
• drone_opt_s_drag_track.py: Drone trajectory tracking with drag force.
• drone_opt_s_drag_delay_track.py: Drone trajectory tracking with drag force and delay.

3. Usage

1. Run the example script:

   python drone_opt_s_drag_delay_track.py

2. You can modify the parameters and models in the script as needed to achieve different control effects.

3. The generated data will be saved in the data/ directory in various formats such as .csv files, including drone_track.csv, drone_state.csv, and drone_control.csv.

4. How to Test the Model

To test the drone model, you can use the drone_simulator.py script. This script simulates the drone's behavior and saves the resulting state and command data.

1. Run the simulation script:

   python drone_simulator.py

2. The script will generate and save the following data files in the data directory:

   • drone_state.csv: Contains the state data of the drone during the simulation.
   • drone_cmd.csv: Contains the command data sent to the drone during the simulation.

3. The script will also plot the states of the drone for visualization.