ppo-b747-step-response

PPO Agent for Boeing 747 Pitch Angle Control

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Proximal Policy Optimization (PPO) for Longitudinal Aircraft Control

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Model Description

This model is a Proximal Policy Optimization (PPO) agent trained to control the pitch angle (θ) of a Boeing 747 aircraft in a longitudinal flight dynamics simulation. The agent receives normalized state observations and outputs continuous elevator deflection commands to track reference pitch angle signals.

Intended Uses

• Primary Use: Automatic pitch angle tracking and stabilization for Boeing 747 aircraft simulation
• Research Applications: Benchmarking RL algorithms for aerospace control systems
• Educational: Learning reinforcement learning concepts in aerospace applications
• Hybrid Control: Can be combined with PID/MPC controllers for robust flight control

Model Architecture

The PPO agent consists of separate Actor and Critic neural networks:

Actor Network (Policy)

Critic Network (Value Function)

State Space

The observation vector consists of 4 normalized states representing the longitudinal dynamics:

Action Space

The normalized action is scaled to physical elevator deflection in degrees by the environment.

Training Details

Training Configuration

Environment Configuration

Training Infrastructure

• Hardware: NVIDIA GPU with CUDA support
• Framework: PyTorch 2.0+
• Training Time: ~7,510 episodes to best checkpoint
• Best Episode: 7,510

Evaluation Results

Performance Metrics

Integral Criteria

Step Response Characteristics

The agent demonstrates excellent step tracking performance with:

• ✅ Minimal overshoot (<1%)
• ✅ Fast settling time (0.6s)
• ✅ Quick rise time (0.3s)
• ✅ Near-zero static error
• ✅ Minimal oscillations (1 cycle)

Usage

Installation

pip install tensoraerospace

Quick Start

import numpy as np
import torch
from tensoraerospace.agent.ppo.model import PPO
from tensoraerospace.envs.b747 import ImprovedB747Env
from tensoraerospace.signals.standart import unit_step
from tensoraerospace.utils import generate_time_period, convert_tp_to_sec_tp

# Load pretrained agent
agent = PPO.from_pretrained("TensorAeroSpace/ppo-b747-pitch-control")

# Setup environment
dt = 0.1
tp = generate_time_period(tn=20, dt=dt)
tps = convert_tp_to_sec_tp(tp, dt=dt)

# Create step reference signal (1 degree step at t=5s)
reference = unit_step(tp=tps, degree=1.0, time_step=5.0, output_rad=True).reshape(1, -1)

env = ImprovedB747Env(
    initial_state=np.array([0.0, 0.0, 0.0, 0.0], dtype=np.float32),
    reference_signal=reference,
    number_time_steps=len(tp),
    dt=dt,
)

# Run evaluation
obs, _ = env.reset()
done = False

while not done:
    action, mean_action, _ = agent.act(obs, deterministic=True)
    action_scalar = float(np.asarray(mean_action).flatten()[0])
    obs, reward, terminated, truncated, info = env.step(action_scalar)
    done = terminated or truncated

Load from Local Checkpoint

from tensoraerospace.agent.ppo.model import PPO

# Load from local directory
agent = PPO.from_pretrained("./path/to/checkpoint")

Limitations

• Fixed Aircraft Model: Trained specifically on Boeing 747 longitudinal dynamics; may not generalize to other aircraft
• Step Reference Only: Optimized for step reference tracking; performance on other signal types (sine, ramp) may vary
• Simulation Gap: Trained in simulation; real-world deployment would require additional validation
• State Observability: Assumes all 4 longitudinal states are observable
• Linear Dynamics: Based on linearized aircraft model around trim conditions

Ethical Considerations

• Not for Real Flight Control: This model is for research and educational purposes only. It should NOT be used for actual aircraft control systems without extensive testing, certification, and regulatory approval.
• Simulation Only: All training and evaluation performed in simulation environments.

Citation

If you use this model in your research, please cite:

@software{tensoraerospace2024,
  title = {TensorAeroSpace: Advanced Aerospace Control Systems \& Reinforcement Learning Framework},
  author = {TensorAeroSpace Team},
  year = {2024},
  url = {https://github.com/TensorAeroSpace/TensorAeroSpace},
  license = {MIT}
}

Model Card Authors

TensorAeroSpace Team

Model Card Contact

• GitHub: TensorAeroSpace/TensorAeroSpace
• Documentation: tensoraerospace.readthedocs.io
• Hugging Face: TensorAeroSpace