drone_mpc

Nonlinear Model Predictive Control (NMPC) for Drone. This implementation is based on acados and inspired by A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight. The visualization is done using MATLAB as shown below:

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_mpc.git
   cd drone_mpc

2. Directory Structure

• drone_opt.py: Contains the implementation of the drone optimizer.
• drone_model.py: Contains the definition of the drone model.

3. Usage

Run the optimizer:

python drone_opt.py

After running drone_opt.py, the drone state and control data files drone_state.csv and drone_control.csv will be generated.

4. Visualization

1. Use the MATLAB script /data_test/data_show.m for visualization.

2. First, import the position state from drone_state.csv as a variable p (shape time_step x 3).

3. Then, run data_show.m in MATLAB to visualize the results.