Tuning PID Controller for Quadrotor Using Particle Swarm Optimization

Abstract

Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for tracking and collaborative navigation tasks. This research aims to establish a basic approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. A PID controller for autonomy provides a straightforward method for correcting robotic movement based on its current state. However, applying a PID system to a flight controller poses challenges with an inherently under-actuated system, which includes the likelihood of large overshoots and lengthy adjustment times. To address this, we utilize PSO (Particle Swarm Optimization) for optimizing PID parameters in a simulated quadrotor. The PSO is employed to find optimal PID values for thrust, yaw, and translational movement on x- and y-positions by identifying converging values across randomly created particles. We conducted a set of experiments and compared it to the default PID controller. The experiments demonstrate converging properties for particles that achieve minimal fitness scores, particularly in reducing overshoot. The results indicate that the optimized PID controller outperforms the default PID controller without optimization. Using optimized PID controllers can decrease the amount of positional error during flight and when adjusting position with collaborative navigation and collision avoidance algorithms.