An Enhanced LQR Control Approach for Optimal Quadrotor Regulation

Abstract Accurate position and altitude control of quadrotor Unmanned Aerial Vehicles (UAVs) is vital for mission-critical applications such as surveillance, defense, and autonomous delivery. This study proposes an enhanced 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 GWO-CS algorithm leverages the global exploration capability of Grey Wolf Optimizer and the local exploitation strength of Cuckoo Search to efficiently determine the optimal weighting matrices of the LQR cost function. A comprehensive nonlinear dynamic model of the quadrotor is developed using Newton–Euler formalism, and the proposed LQR–GWO-CS controller is implemented and tested in MATLAB/Simulink. Simulation results demonstrate that the proposed controller outperforms conventional LQR, LQR-GWO, and LQR-WOA approaches in terms of settling time, overshoot, and integral absolute error (IAE). Additionally, robustness and disturbance rejection capabilities were validated through real-time simulation on OPAL-RT, confirming the controller’s applicability to practical UAV operations. These findings establish the LQR–GWO-CS framework as a robust, accurate, and efficient control solution for UAVs in dynamic and uncertain environments.