Genetic Algorithm Based PID Parameter Optimization for Longitudinal Dynamics of a Fixed Wing Mini UAV

Abstract

Unmanned aerial vehicles (UAVs) have gained very much importance in both civilian and industrial areas. Especially, in military applications, there are growing demands on the UAVs’ usage for reconnaissance, surveillance, defence, and attack purposes. Therefore, autonomous control of the UAVs has great importance to achieve stable and successful missions. Most of the control techniques in the aircraft control systems use the classical PID (proportional + integral + derivative) control method but this method may have some insufficient response characteristics in terms of changing weather conditions and disturbances. At this point, because of the PID usage in most UAV systems, PID controller parameters must have been obtained for optimum control performance of any state dynamics of the UAV. In this study, longitudinal dynamics are obtained for pitch angle and altitude dynamics of a mini fixed-wing Cessna-182 UAV at 60 km/h flight speed. PID parameters are investigated for the best performance of evolutional genetic algorithms (GA) in terms of different performance indexes such as integrated square error (ISE), integrated absolute error (IAE), time multiply of square error and absolute errors (ITSE, ITAE) and linear quadratic regulator (LQR) cost functions. The simulation results indicate the best performance obtained for the LQR index in the point of overshoot and settling time. In addition, proportioned hybrid ITSE and ITAE cost also developed their performance either for overshoot or settling time. This paper contributes to sustainable development goal (SDG9) by investigating aerial vehicle innovation for a stable and reliable flight purpose in the industrial and civilian applications.