Hybrid grey Wolf-Cuckoo search optimized linear quadratic regulator for robust quadrotor control

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence and Optimization Algorithms
• Aerospace Engineering

Background:

• Precise control of quadrotor Unmanned Aerial Vehicles (UAVs) is critical for demanding applications like surveillance and autonomous delivery.
• Existing control methods often face challenges in achieving optimal performance and robustness in dynamic environments.

Purpose of the Study:

• To develop and validate an advanced control framework for quadrotor UAVs.
• To enhance position and altitude control accuracy and efficiency using a hybrid optimization algorithm.

Main Methods:

• Developed a nonlinear dynamic model of the quadrotor using the Newton-Euler formalism.
• Integrated a Linear Quadratic Regulator (LQR) with a hybrid Grey Wolf Optimizer-Cuckoo Search (GWO-CS) algorithm for optimal LQR gain tuning.
• Implemented and tested the LQR-GWO-CS controller in a simulated environment and via Hardware-in-the-Loop (HIL) testing.

Main Results:

• The LQR-GWO-CS controller achieved significantly reduced settling times and zero overshoot on the X and Y axes compared to conventional LQR.
• Integral Absolute Error (IAE) was reduced by approximately 39% on the X-axis and improved from 1.16 to 0.70 on the Y-axis.
• Altitude control demonstrated a reduction in settling time from 4.27s to 1.96s with limited overshoot (2.0%), outperforming other tested methods.

Conclusions:

• The proposed LQR-GWO-CS framework offers a robust, efficient, and quantitatively validated solution for quadrotor UAV control.
• The hybrid GWO-CS algorithm effectively tunes LQR gains, leading to superior performance in position and altitude stabilization.
• The controller's feasibility for practical UAV missions is confirmed through simulation and HIL testing under external disturbances.