Precision enhancement in drone position control based on optimized PID: A comparative study

Abstract

This paper presents a comparative study of quadrotor trajectory tracking performance using proportional integral derivative (PID) controllers optimized by two nature-inspired metaheuristic algorithms: the flower pollination algorithm (FPA) and particle swarm optimization (PSO). A nonlinear dynamic model of the quadrotor is first established, capturing both translational and rotational motions. A cascaded PID control architecture is then designed, where the outer loop regulates position and the inner loop controls attitude and altitude. The PID gains are tuned offline by minimizing an objective function combining the integral of time-weighted absolute error (ITAE) and an overshoot penalization term. The optimized controllers are evaluated through three reference scenarios: step response, circular trajectory, and lemniscate trajectory. Quantitative performance metrics, including RMSE, MAE, IAE, and ITAE, are used to assess tracking accuracy. Results show that the PID-FPA controller significantly outperforms PID-PSO in terms of overshoot reduction, faster settling time, smoother responses, and improved tracking precision, especially for complex trajectories. A robustness analysis is finally conducted by introducing a realistic synthetic wind disturbance modeled using sinusoidal components and stochastic turbulence. The FPA-tuned PID demonstrates strong robustness, maintaining accurate trajectory tracking under aerodynamic perturbations.