Path Following Control Tuning for an Autonomous Unmanned Quadrotor Using Particle Swarm Optimization

The goal of this work is to present the particle swarm optimization application for quadrotor attitude and path following control tuning. To perform this task a path following feed-forward plus proportional-derivative control strategy was implemented, using particle swarm for tuning gains, and root mean square error for validating. The basic of quadrotor kinematics and dynamics model will be presented. Path planning will be executed through Euler-Lagrange equations to minimize the snap cost function and guarantee a soft trajectory through a set of intermediary waypoints. The reliability of this approach will be tested through several simulations.