Research on PID Control Parameter Tuning of Quadrotor UAV Based on Improved Intelligent Optimization Algorithm

Quadrotor unmanned aerial vehicles (UAVs) are nonlinear, strongly coupled, and underactuated systems, making precise attitude control crucial for stable flight and mission efficiency. Traditional PID parameter tuning methods rely heavily on manual experience, often resulting in suboptimal performance. This paper proposes a PID parameter tuning approach based on an improved particle swarm optimization (PSO) algorithm, integrating adaptive inertia weight, dynamic learning factors, and Latin hypercube sampling to enhance optimization efficiency and convergence. A cascade PID control structure is designed, with an outer loop for attitude control and an inner loop for angular velocity control. Simulation and experimental results demonstrate that the proposed method effectively improves settling time and reduces overshoot, ensuring robust and stable UAV attitude control in various flight conditions. The study provides a practical solution for efficient and reliable PID tuning in quadrotor UAV systems.