tensoraerospace 0.3.3

Open source deep learning framework that focuses on aerospace objects (rockets, planes, UAVs)

🚀 TensorAeroSpace

Advanced Aerospace Control Systems & Reinforcement Learning Framework

A comprehensive Python library for aerospace simulation, control algorithms, and reinforcement learning implementations

📖 Documentation • 🚀 Quick Start • 💡 Examples • 🤝 Contributing

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🌟 Overview

TensorAeroSpace is a cutting-edge Python framework that combines aerospace engineering with modern machine learning. It provides:

• 🎯 Control Systems: Advanced control algorithms including PID, MPC, and modern RL approaches
• ✈️ Aerospace Models: High-fidelity aircraft and spacecraft simulation models
• 🎮 OpenAI Gym Integration: Ready-to-use environments for reinforcement learning
• 🧠 RL Algorithms: State-of-the-art reinforcement learning implementations
• 🔧 Extensible Architecture: Easy to extend and customize for your specific needs

🚀 Quick Start

📦 Installation

Using Poetry (Recommended)

git clone https://github.com/tensoraerospace/tensoraerospace.git
cd tensoraerospace
poetry install

Using pip

pip install tensoraerospace

🐳 Docker

docker build -t tensoraerospace . --platform=linux/amd64
docker run -v $(pwd)/example:/app/example -p 8888:8888 -it tensoraerospace

🏃‍♂️ Quick Example

import tensoraerospace as tas

# Create an F-16 environment
env = tas.envs.F16Env()

# Initialize a PPO agent
agent = tas.agent.PPO(env.observation_space, env.action_space)

# Train the agent
for episode in range(1000):
    obs = env.reset()
    done = False
    while not done:
        action = agent.act(obs)
        obs, reward, done, info = env.step(action)

🤖 Supported Algorithms

Algorithm	Type	HuggingFace Export	Status
IHDP	Incremental Heuristic Dynamic Programming	❌	✅
DQN	Deep Q-Learning	❌	✅
DDPG	Deep Deterministic Policy Gradient	❌	✅
SAC	Soft Actor-Critic	✅	✅
A3C	Asynchronous Advantage Actor-Critic	❌	✅
PPO	Proximal Policy Optimization	✅	✅
GAIL	Imitation Learning (Adversarial)	❌	✅
MPC	Model Predictive Control	✅	✅
A2C	Advantage Actor-Critic	✅	✅
A2C-NARX	A2C with NARX Critic	❌	✅
PID	Proportional-Integral-Derivative	✅	✅

✈️ Aircraft & Spacecraft Models

🛩️ Fixed-Wing Aircraft

• General Dynamics F-16 Fighting Falcon - High-fidelity fighter jet model
• Boeing 747 - Commercial airliner dynamics
• McDonnell Douglas F-4C Phantom II - Military aircraft model
• North American X-15 - Hypersonic research aircraft

🚁 UAVs & Drones

• LAPAN Surveillance Aircraft (LSU)-05 - Indonesian surveillance UAV
• Ultrastick-25e - RC aircraft model
• Generic UAV State Space - Configurable UAV dynamics

🚀 Rockets & Satellites

• ELV (Expendable Launch Vehicle) - Launch vehicle dynamics
• Generic Rocket Model - Customizable rocket simulation
• Geostationary Satellite - Orbital mechanics simulation
• Communication Satellite - ComSat dynamics and control

🎮 Simulation Environments

🎯 Unity ML-Agents Integration

TensorAeroSpace seamlessly integrates with Unity ML-Agents for immersive 3D simulations:

• 🎮 3D Visualization: Real-time 3D aircraft simulation
• 🔄 Real-time Training: Train agents in realistic environments
• 📊 Rich Sensors: Camera, LiDAR, and physics-based sensors
• 🌍 Custom Environments: Build your own aerospace scenarios

📁 Example Environment: UnityAirplaneEnvironment

🔧 MATLAB Simulink Support

• 📐 Model Import: Convert Simulink models to Python
• ⚡ High Performance: Compiled C++ integration
• 🔄 Bidirectional: MATLAB ↔ Python workflow
• 📊 Validation: Cross-platform model validation

📊 State Space Matrices

Mathematical foundation for control system design:

• 🧮 Linear Models: State-space representation
• 🎛️ Control Design: Modern control theory implementation
• 📈 Analysis Tools: Stability, controllability, observability
• 🔄 Linearization: Nonlinear model linearization

📚 Examples & Tutorials

Explore our comprehensive example collection in the ./example directory:

Category	Description	Notebooks
🚀 Quick Start	Basic usage and concepts	quickstart.ipynb
🤖 Reinforcement Learning	RL algorithm implementations	reinforcement_learning/
🎛️ Control Systems	PID, MPC controllers	pid_controllers/, mpc_controllers/
✈️ Aircraft Models	Environment examples	environments/
🔧 Optimization	Hyperparameter tuning	optimization/

🛠️ Development & Contributing

We welcome contributions! Please see our Contributing Guide for details.

🏗️ Development Setup

git clone https://github.com/tensoraerospace/tensoraerospace.git
cd tensoraerospace
poetry install --with dev
poetry run pytest  # Run tests

🧪 Testing

# Run all tests
poetry run pytest

# Run specific test category
poetry run pytest tests/envs/
poetry run pytest tests/agents/

📖 Documentation

• 📚 Full Documentation: tensoraerospace.readthedocs.io
• 🚀 API Reference: Detailed API documentation
• 📝 Tutorials: Step-by-step guides
• 💡 Examples: Practical use cases

🤝 Community & Support

• 💬 Discussions: GitHub Discussions
• 🐛 Issues: Bug Reports
• 📧 Contact: Email Support

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenAI Gym team for the excellent RL framework
• Unity ML-Agents team for 3D simulation capabilities
• The aerospace engineering community for domain expertise
• All contributors who make this project possible

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