2DOF Helicopter LSTM

Overview

This project models the nonlinear dynamics of a 2-DOF helicopter system using a Long Short-Term Memory (LSTM) neural network. The model predicts the system's behavior based on sequential input variables (voltage and motor currents) to output pitch and yaw angles.

Colab Link

You can access the Google Colab notebook for this project:

Data Used

The model is trained and validated using real sequential data representing the helicopter's behavior:

• Input Variables:
  • V: Voltage applied to the motors (constant for both motors).
  • I_pitch: Current for the pitch motor.
  • I_yaw: Current for the yaw motor.
• Output Variables:
  • pitch_angle (θ): The pitch angle of the helicopter.
  • yaw_angle (ψ): The yaw angle of the helicopter.

Data Format

The input and output data are stored in separate CSV files:

• inputs.csv: Contains columns V, I_pitch, I_yaw.
• outputs.csv: Contains columns pitch_angle, yaw_angle.

Using the Trained Model

• Ensure the trained model file 2dof_helicopter_model.h5 is available in your working directory or uploaded to Colab.
• Use the model to make predictions:

  # Define new input (1 timestep, 3 features: V, I_pitch, I_yaw)
  new_inputs = [[15, 2, 2]]  # Example input