Optimal Tuning of FOPID Controller for a MIMO Aircraft Model Using Swarm Intelligence

Abstract

Control systems of aircraft dynamics focus on choosing configurations that meet certain criteria related to speed response, damping ratio, error in steady state, among others. Aeronautical engineering applications have safety requirements, and in this respect, the use of optimization algorithms assist in the ability to develop models of increasing complexity. Therefore, the main objective of this work is to develop a methodology for optimal tuning of fractional order proportional integral derivative (FOPID) controller based on particle swarm optimization (PSO) for multiple input multiple output (MIMO) aircraft model. This work can contribute to the aeronautical performance through the development of simulations seeking the best parameters from the controller, allowing flight conditions according to pre-established requirements. Three stochastic optimization algorithms were studied: global random search (GRS), local random search (LRS) and PSO. The PSO delivered the best results. Different performance indices based on the integral of error were used as objective functions. The ITAE (integral of time multiplied by the absolute error) index was chosen for presenting the best performance. It was necessary to limit the input values of the models, taking into account the limitation of the integrator term of the controller based on the anti-windup (AW) technique. Based on the prerequisites for the aircraft, the performance metrics achieved results considered adequate to the challeging demands of the task.