Quadrotor Attitude Tracking using MRAC & LQR | MATLAB Simulink UAV Control Simulation
This video presents a Quadrotor UAV Attitude Tracking Control System implemented in MATLAB/Simulink, using Model Reference Adaptive Control (MRAC) and Linear Quadratic Regulator (LQR) techniques. The simulation demonstrates precise roll, pitch, and yaw angle tracking under disturbances, uncertainties, and parameter variations.
The MRAC controller enables the quadrotor to adapt online to system uncertainties by forcing the plant to follow a predefined reference model, while the LQR controller provides optimal state-feedback control with minimum control effort and improved stability.
The model includes nonlinear quadrotor dynamics, attitude kinematics, reference model generation, adaptive law, LQR gain computation, and performance comparison between MRAC and LQR controllers.
This project is highly suitable for final-year engineering projects, MTech/MS theses, PhD research, UAV control studies, and IEEE journal-oriented simulations.
📞 MATLAB / Simulink Project & Research Support
🌐 Website: www.matlabprojectscode.com
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🔧 Key Features
✅ Nonlinear quadrotor attitude dynamics
✅ Roll, pitch & yaw tracking control
✅ Model Reference Adaptive Control (MRAC)
✅ Optimal control using LQR
✅ Robust performance under disturbances
✅ MATLAB/Simulink block-level implementation
✅ Comparative analysis of MRAC vs LQR
🎓 Who Should Watch?
Aerospace & Electrical Engineering Students
UAV & Drone Control Researchers
MATLAB / Simulink Learners
Control Systems Engineers
Master’s & PhD Scholars
🚁 Applications
Autonomous quadrotor drones
UAV attitude stabilization
Military & surveillance drones
Robotics & aerial vehicles
Adaptive and optimal control research
We provide complete MATLAB/Simulink models, IEEE paper guidance, thesis support, and custom control algorithms for students and researchers worldwide (India, UK, USA, UAE, Australia, Europe).
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🔖 YouTube Tags (Comma-Separated, SEO Ready)
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🔖 Hashtags (Optional for Description)
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#LQR
#AdaptiveControl
#MATLAB
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