Thrust Vectoring Flight Control System

A longitudinal aircraft dynamics simulation with thrust vectoring control, demonstrating gain-scheduled PID control and control allocation across the flight envelope.

🎯 Project Overview

This project implements a complete 2D (longitudinal) flight control system for an F-16 class fighter aircraft with thrust vectoring capability. It demonstrates:

• Realistic 6DOF longitudinal dynamics with nonlinear aerodynamics
• Gain-scheduled PID control adapted to angle of attack
• Control allocation between elevator and thrust vectoring nozzle
• Professional numerical integration using scipy's adaptive RK45 solver

✨ Key Features

• ✅ Trimmed flight calculation using nonlinear optimization
• ✅ High-fidelity aerodynamics from tabulated coefficient data (α = -10° to 90°)
• ✅ Thrust vectoring model with ±20° nozzle deflection limits
• ✅ Adaptive control allocation that shifts authority based on flight condition
• ✅ Robust integration with automatic error control (rtol=1e-6)

📊 Demonstration Results

Scenario 1: Low Alpha Flight (200 m/s)

• RMS Tracking Error: 0.119 deg/s (near-perfect)
• Control Strategy: Elevator dominant (conventional control sufficient)
• Nozzle Usage: 0% (not needed)

Scenario 2: Post-Stall Upset Recovery (35°, 80 m/s)

• RMS Tracking Error: 4.945 deg/s (acceptable for extreme upset)
• Control Strategy: Thrust vectoring dominant (100% of time)
• Nozzle Usage: Up to 5.46° deflection

Key Insight: Thrust vectoring provides essential control authority during post-stall upset recovery where conventional surfaces are ineffective.

🚀 Quick Start

Installation

git clone https://github.com/Kumar-S-Bhat/thrust-vectoring
cd thrust-vectoring
pip install -r requirements.txt

Run Demonstrations

# Pitch rate tracking demonstration
python examples/q_tracking.py

# View results
# Plots saved as: q_tracking_comparison.png

📂 Project Structure

thrust-vectoring/
├── src/
│   ├── aircraft/
│   │   ├── dynamics.py          # 6DOF longitudinal equations
│   │   ├── aerodynamics.py      # Aero coefficient tables
│   │   └── frames.py            # Coordinate transformations
│   ├── propulsion/
│   │   ├── thrust_model.py      # Engine thrust model
│   │   ├── nozzle.py            # TVC system
│   │   └── actuator.py          # Nozzle actuator dynamics
│   ├── control/
│   │   ├── pid.py               # PID controller with anti-windup
│   │   ├── gain_scheduled.py    # Gain-scheduled PID
│   │   ├── allocator.py         # Control allocation
│   │   └── pitch_controller.py  # Longitudinal controller
│   └── simulation/
│       └── trim_solver.py       # Trim condition solver
├── examples/
│   ├── q_tracking.py            # Pitch rate tracking demo
│   ├── nozzle_step.py           # Actuator response test
│   ├── simulate_flight.py       # Open-loop flight simulation
│   └── tune_pid_nozzle.py       # PID gain tuning
├── tests/
│   ├── test_actuator_response.py
│   ├── test_dynamics.py
│   ├── test_pid_response.py
│   ├── test_thrust_model.py
│   └── test_trim.py
├── data/
│   └── aero_tables.csv          # Aerodynamic coefficient data
├── q_tracking_comparison.png     # Results visualization
├── requirements.txt
├── LICENSE
├── .gitignore
└── README.md

🔧 Technical Details

Aircraft Model

• Type: F-16 class fighter
• Mass: 9,300 kg
• Pitch Inertia: 55,814 kg·m²
• Wing Area: 27.87 m²
• Thrust: 130 kN (sea level static)

Control System

• Outer Loop: Proportional alpha-to-rate controller (Kp = 3.0)
• Inner Loop: Gain-scheduled pitch rate PID
  • Kp: 10,000,000 - 200,000 N·m (varies with α)
  • Ki: 6,000,000 - 10,000 N·m·s (decreases at high α)
  • Kd: 300,000 - 3,500,000 N·m/s (increases with α)

Numerical Integration

• Method: scipy.integrate.solve_ivp with RK45
• Tolerance: rtol=1e-6, atol=1e-9
• Advantages: Adaptive step sizing, guaranteed error bounds

📈 Results Analysis

Control Surface Deflections

Scenario	Elevator (max)	Nozzle (max)	Dominant Control
Low Alpha (200 m/s)	0.30°	0.00°	Elevator
Post-Stall Upset (35°)	0.75°	5.46°	Thrust Vectoring

Tracking Performance

The system achieves excellent tracking at low angle of attack where aerodynamics are well-behaved. At post-stall conditions (35° alpha), tracking degrades but the system successfully recovers from the upset using thrust vectoring.

🎓 Educational Value

This project demonstrates:

1. Aircraft Dynamics Modeling

   • 6DOF equations in body frame
   • Aerodynamic force/moment calculation
   • Thrust vectoring kinematics

2. Control System Design

   • Gain scheduling for nonlinear systems
   • Control allocation for over-actuated systems
   • PID tuning and anti-windup

3. Numerical Methods

   • ODE integration with error control
   • Trim solving via nonlinear optimization
   • Professional simulation architecture

🔬 Limitations & Future Work

Current Limitations

• 2D Only: Longitudinal dynamics only (no roll/yaw)
• Simplified Aerodynamics: 1D tables (no β, Mach effects)
• No Actuator Lag: Instant control response (simplified)
• Linear Control Basis: PID struggles at extreme angles (>40°)

Suggested Improvements

1. Full 6DOF Dynamics

   • Add lateral-directional equations
   • Implement aileron and rudder
   • Model inertia coupling effects

2. Advanced Control

   • Dynamic inversion for nonlinear regimes
   • LQR for optimal gains
   • Adaptive control for robustness

3. Enhanced Realism

   • Actuator dynamics (rate limits, lag)
   • Sensor noise and filtering
   • Atmospheric turbulence

4. Extended Analysis

   • Modal analysis (eigenvalues, damping ratios)
   • Stability margins
   • Flight envelope exploration

📚 References

Textbooks

1. Stevens, B. L., & Lewis, F. L. (2003). Aircraft Control and Simulation. Wiley.
2. Nelson, R. C. (1998). Flight Stability and Automatic Control. McGraw-Hill.
3. Etkin, B., & Reid, L. D. (1996). Dynamics of Flight: Stability and Control. Wiley.

Aerodynamic Data

• NASA Technical Reports on F-16 aerodynamics
• Anderson, J. D. (2017). Fundamentals of Aerodynamics. McGraw-Hill.

Control Theory

• Ogata, K. (2010). Modern Control Engineering. Prentice Hall.
• Åström, K. J., & Murray, R. M. (2021). Feedback Systems. Princeton University Press.

👨‍💻 Author

Kumar S Bhat
Aerospace Engineering Student
Indian Institute of Engineering Science and Technology, Shibpur

LinkedIn: https://www.linkedin.com/in/kumar-bhat-6337a7332/
GitHub: https://github.com/Kumar-S-Bhat

📄 License

MIT License - See LICENSE file for details.

🙏 Acknowledgments

• Aircraft parameters based on publicly available F-16 data
• Control architecture inspired by modern fighter flight control systems
• Project developed as part of aerospace engineering coursework

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This project demonstrates aerospace engineering principles including flight dynamics, control systems, and numerical simulation. It is intended for educational purposes.