Performance comparisons of self-adaptive artificial intelligence control algorithms designed using particle swarm optimization in aircraft takeoff and landing

Abstract

In this study, adaptive artificial intelligence control algorithms were designed using the particle swarm optimisation (PSO) method for safe take-off and landing of aircraft. The performance of the designed control algorithms, which were applied to the control of the aircraft’s pitch angle, was compared with each other and with studies in the literature. Integrated squared error (ISE) was used as a fitness function in the design of PSO-based proportional–integral–derivative (PSO-PID), PSO-based fuzzy logic controller (PSO-FLC) and PSO-based self-adaptive fuzzy-PID (PSO-SAF-PID) controllers. Performance comparisons between the designed controllers were made based on the transient behaviours such as overshoot, rise time and settling time, and statistical error analyses including mean squared error (MSE), mean absolute error (MAE), ISE and integrated absolute error (IAE). It was observed that the performance of the PID controller, FLC and SAF-PID controllers improved significantly with the application of the PSO method. First, the mathematical model of the system was derived. Second, traditional control methods (PID control algorithm) and artificial intelligence control methods (FLC and SAF-PID type control algorithms) were designed to control the modelled system. Then, to improve the performance of these designed control algorithms, the optimal control parameters of the PID algorithm were determined using the PSO method. At the same time, membership function weight coefficients of the FLC algorithm and SAF-PID type control algorithm were determined using the PSO algorithm. Determining the control parameters and membership function coefficients at optimum values significantly affects the performance of control algorithms. Optimal control parameters and membership coefficients have significantly improved control performance. Furthermore, tests performed on transfer functions with different system parameters have shown that the PSO-SAF-PID controller is robust.