🚀 Spacecraft_HIL_Simulation

A real-time Hardware-in-the-Loop (HIL) simulation for spacecraft avionics — built in Python.
This project simulates a gyroscope sensor and a basic flight computer, allowing real-time interaction to modify signal parameters mid-simulation.

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📌 Features

• Simulates gyroscope sensor with sine + Gaussian noise
• Real-time interactive control: pause, resume, stop, and change signal parameters
• Decision logic to simulate basic flight computer actions
• Outputs:
  • Time-series plot of gyroscope data with control thresholds
  • Command log file

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🧠 How It Works

1. Sensor model generates a noisy sine-wave signal as gyroscope output.
2. Flight computer analyzes the signal and makes decisions like:
   • Activate Dampers
   • Stabilize Yaw
   • Hold Position
3. You can interact with the simulation in real time via terminal input:
   • pause, resume, stop
   • amp <value>, freq <value>, noise <value>

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🖥️ Requirements

• Python 3.x
• numpy
• matplotlib

Install required libraries:

pip install numpy matplotlib

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▶️ Run the Simulation

python hil_simulation.py

During simulation, you can type:

• pause — Pauses simulation
• resume — Resumes simulation
• stop — Ends simulation
• amp 1.5 — Set signal amplitude
• freq 0.8 — Set signal frequency
• noise 0.2 — Set noise standard deviation

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📈 Sample Output

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📂 Output Files

• gyro_data_interactive.png — Plot of gyroscope readings vs. time
• hil_simulation_log.txt — Logged commands and readings in CSV format

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📁 Project Structure

Spacecraft_HIL_Simulation/
├── hil_simulation.py           # Main simulation file
├── README.md                   # This file
├── gyro_data_interactive.png   # Output plot (auto-generated)
└── hil_simulation_log.txt      # Output log (auto-generated)

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🛠️ Future Ideas

• Add GUI using Streamlit or Tkinter
• Simulate more sensors (e.g., accelerometers, magnetometers)
• Integrate with real spacecraft hardware for testing

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👨‍💻 Author

Arhaan Khan

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📝 License

This project is licensed under the MIT License.

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Made with 💡 for simulation testing, space systems learning, and real-time control exploration.