A Hierarchical PSMC-LQR Control Framework for Accurate Quadrotor Trajectory Tracking.

Area of Science:

• Robotics
• Control Systems Engineering
• Aerospace Engineering

Background:

• Quadrotor UAVs face challenges in accurate trajectory tracking due to nonlinear dynamics, uncertainties, and external disturbances.
• Achieving precise position tracking and stable attitude regulation simultaneously under control constraints is difficult.

Purpose of the Study:

• To develop a hierarchical control framework for enhanced quadrotor UAV trajectory tracking.
• To improve robustness against model uncertainties and external disturbances.
• To ensure precise position tracking and stable attitude regulation.

Main Methods:

• A hierarchical control framework combining Particle Swarm Optimization (PSO)-compensated Model Predictive Controller (PSMC) and an enhanced Linear Quadratic Regulator (LQR).
• Outer-loop PSMC adaptively mitigates prediction errors and enhances robustness.
• Inner-loop LQR with gain scheduling and control-rate relaxation accelerates attitude convergence and ensures smooth control.

Main Results:

• The proposed framework reduced mean tracking errors by over 13.2%, 17.1%, and 28% in x, y, and z directions under calm conditions compared to a conventional MPC-LQR baseline.
• Under wind disturbances, the framework reduced mean tracking errors by over 34%, 26.2%, and 46.8% in x, y, and z directions.
• Lyapunov-based stability analysis confirmed closed-loop convergence.

Conclusions:

• The hierarchical PSMC-LQR framework offers superior trajectory tracking accuracy for quadrotor UAVs.
• The proposed method demonstrates strong robustness against external disturbances and model uncertainties.
• The framework exhibits high practical implementability for real-world quadrotor control applications.