OPTIMAL TUNING OF PID CONTROLLER FOR QUADROTOR SYSTEM USING A NEW ADAPTIVE PARTICLE SWARM OPTIMIZATION

Abstract: In this study, a detailed mathematical nonlinear dynamic model of the quadcopter system which is derived using the Newton-Euler method. PID controllers are used for controlling the roll, pitch, yaw, and altitude movements of the quadcopter. Manual tuning of PID controllers does not always give acceptable results, consume a long time, and difficult. Therefore, the tuning process of PID controllers is done by particle swarm optimization algorithm (PSO). A new adaptive particle swarm optimization (APSO) algorithm that gives better search efficiency and convergence speed than standard particle swarm optimization is suggested. It enables the automatic control of inertia weight which controls the global and local search abilities of the PSO algorithm. Comparing with the trial and error method and standard PSO algorithm, the adaptive PSO algorithm gives better performance in terms of convergence speed and permanent movement toward the optimal solution region.