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

Accurate position and altitude control of quadrotor Unmanned Aerial Vehicles (UAVs) is essential for mission-critical applications such as surveillance, defense, and autonomous delivery. This study introduces an innovative control framework that integrates a Linear Quadratic Regulator (LQR) with a hybrid Grey Wolf Optimizer-Cuckoo Search (GWO-CS) algorithm for optimal gain tuning. The innovation lies in combining GWO's global exploration with CS's local exploitation, ensuring faster convergence and higher-quality tuning of LQR weighting matrices. A comprehensive nonlinear dynamic model of the quadrotor was developed using the Newton-Euler formalism, and the LQR-GWO-CS controller was implemented in a simulated environment. Comparative analysis reveals that the proposed controller achieves significant improvements. For the X-axis, the settling time is reduced from 4.08 s (LQR) and 7.36 s (LQR-GWO) to 1.70 s with zero overshoot, while the Integral Absolute Error (IAE) improves by approximately 39% compared to the conventional LQR. For the Y-axis, the proposed method reduced the IAE from 1.16 (LQR) to 0.70 with a settling time of 1.64 s and zero overshoot, outperforming LQR-WOA, which exhibited 4.2% overshoot. In altitude (Z-axis) control, the proposed controller limited overshoot to 2.0% while reducing settling time from 4.27 s (LQR) to 1.96 s, with lower IAE than both LQR and LQR-WOA. Robustness was further demonstrated under external disturbances and validated through real-time Hardware-in-the-Loop testing on OPAL-RT (Operational and Automation Platform for Real-Time applications), confirming feasibility for practical UAV missions. Overall, the LQR-GWO-CS framework outperforms state-of-the-art controllers, offering a quantitatively validated, robust, and efficient solution for UAV operation in dynamic and uncertain environments.