UAV Longitudinal Flight Dynamics Simulator image

UAV Longitudinal Flight Dynamics Simulator

Project Overview

A technical report for our open-source UAV longitudinal flight dynamics simulator project. This report covers UAV modeling, input control, state-space simulation, visualization systems, aerodynamic and stability analyses, sensor integration, safety features, and system requirements.

Skills Used

Aerospace Engineering Flight Dynamics Python Control Systems Data Visualization

Project Overview

The UAV Longitudinal Flight Dynamics Simulator is an open-source Python tool developed to simulate and visualize the longitudinal (pitch-axis) flight behavior of UAVs. This report documents the development and testing of the simulation system, covering UAV modeling, aerodynamic and control analysis, and real-time visualizations. It also outlines the integration of elevator input profiles and system stability assessment tools.

1. UAV Modeling, Simulation & Visualization

1.1 UAV Specifications

  • Supported Models:
    • Baykar TB2
    • TUSAŞ Anka
    • Aksungur
    • Vestel Karayel
    • General Atomics Predator
    • IAI Heron
  • Flight Axis Simulated:
    • Longitudinal (Pitch Axis)

1.2 Simulation Overview

The tool enables users to simulate the pitch-axis response of UAVs to various elevator input types. Key aspects include:

  • State-Space Modeling:
    UAVs are modeled using linearized state-space representations.
  • Numerical Integration:
    The system uses Runge-Kutta methods (scipy.solve_ivp) to simulate time-domain responses.
  • Visualization:
    2D plots and 3D phase-space visualizations show state evolution.

Example Diagram:

figure_1_3D.png

2. Input Control & Flight Data

The simulator supports different elevator input types and computes the corresponding flight responses:

  • Step Input:
    Simulates a sudden elevator deflection.
  • Doublet Input:
    Used to analyze damping and short-period response.
  • Custom Input:
    User-defined elevator time-series input.

Each input profile affects pitch rate, angle of attack, forward velocity deviation, and pitch angle, which are visualized in simulation outputs.

3. Sensor Integration & Control Feedback

Although the simulation operates in software, sensor models and control feedback are considered:

  • Virtual IMU Simulation:
    Simulates output from a 10 DOF IMU (acceleration, gyro, orientation).
  • Virtual Sensor Models:
    Mimics behavior of realistic flight sensors to allow for control loop testing.
  • Control Feedback (Planned):
    PID/State-feedback control modules are under development for closed-loop response testing.

4. Aerodynamics & Stability Analysis

Aerodynamic and stability parameters are derived from each UAV’s geometry and coefficients:

  • Aerodynamic Database:
    Pre-loaded aerodynamic models for each UAV.
  • Mode Identification:
    The system detects and characterizes phugoid and short-period modes.
  • Stability Metrics:
    Calculates eigenvalues, natural frequency, and damping ratio in real-time.

5. Propulsion & System Characteristics

5.1 Propulsion Approximation

While propulsion is not actively modeled in longitudinal analysis, a constant-speed assumption is used:

  • Speed Preset:
    Simulated UAVs fly at trimmed cruise conditions (~100–200 km/h depending on model).
  • Disturbance Response:
    Inputs affect longitudinal axis only; propulsion modeled as steady-state.

5.2 System Constraints

  • Numerical Stability:
    Integration step size and stiffness are managed by solve_ivp.
  • Data Scaling:
    Inputs and outputs are normalized for visualization.

6. Visual Output & Result Interpretation

6.1 2D Time-Domain Plots

Plots include:

  • Forward speed deviation (u)
  • Angle of attack (α)
  • Pitch rate (q)
  • Pitch angle (θ)
  • Elevator input

6.2 3D Phase-Space Visualization

A 3D plot shows evolution in the α–q–θ state-space, highlighting stability characteristics and control response.

7. Safety Checks & Simulation Limits

To ensure accurate and safe simulations, the following checks are implemented:

  • Numerical Instability Detection:
    Alerts users if simulation diverges.
  • State Clipping:
    Prevents unrealistic values from visualizing.
  • Input Validation:
    Elevator inputs are checked for format and range.

8. System Architecture & Communication

The simulation tool is modular and extendable:

  • Language:
    Python 3.x
  • Libraries Used:
    NumPy, SciPy, Matplotlib
  • Architecture:
    Input parser → State-space solver → Visualizer

9. Performance & Development Roadmap

9.1 Performance Notes

The simulation is optimized for real-time or near real-time operation on standard machines.

9.2 Future Work

  • Closed-loop PID and LQR control modules
  • GUI frontend
  • Real-world flight data calibration
  • Extended axes (lateral-directional simulation)

Appendix

For full source code, documentation, and UAV model definitions, visit the GitHub repository. Github

Project by Dağlar Duman, 2025