Robustness Analysis of LQR-PID Controller Based on PSO and GWO for Quadcopter Attitude Stabilization

The robust control of quadcopters is essential for maintaining stability and performance in dynamic environments. This paper examines the effectiveness of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for tuning LQR-PID controllers tailored for a quadcopter constrained to rotational degrees of freedom, aiming to enhance attitude stabilization and perform a comparative robustness analysis under various disturbances. Using PSO and GWO to optimize the LQR controller’s Q and R matrices, the study minimizes a cost function based on attitude error and control effort. The optimized controllers are evaluated in a Simulink environment with external perturbation forces; noise perturbations and sudden impulse disturbances are introduced via feedback vector perturbations to simulate real-world operational challenges. The results reveal distinct robustness characteristics: the PSO-optimized controller achieves faster convergence with higher sensitivity to disturbances, while the GWO-optimized controller performs better under extreme parameter variations. By providing a detailed comparison of these optimization techniques, the study offers valuable insights into selecting the most suitable method for robust and reliable quadcopter attitude control.