Robust trajectory tracking control of a quadrotor under external disturbances and dynamic parameter uncertainties using a hybrid P-PID controller tuned with ant colony optimization

Abstract

Controlling the dynamic model of a nonlinear quadrotor system is complex, and manually tuning the controller gains is challenging due to the intricate relationships between dynamic variables and the influence of external disturbances on performance. In this study, we propose designing an improved controller using the ant colony optimization (ACO) method, with the help of an objective function composed of output signal parameters. This method involves determining the optimal gains for the six controllers within the quadrotor model. The aim is to solve stability problems encountered during trajectory tracking under external disturbances and uncertainties of dynamic parameters. First, a model of a nonlinear multi-input, multi-output (MIMO) quadrotor system consisting of four inputs and six outputs was studied. Then, a hybrid controller combining a proportional controller and a proportional-integral-derivative controller (P-PID) was developed to manage the six degrees of freedom (6–DOF) during trajectory tracking. The improved ACO method is employed to tune the controller gain values by minimizing an objective function, with the aim of reducing the error between output and input signals and optimizing the system parameters. Finally, the quadrotor model was simulated to assess the robustness and effectiveness of the proposed P-PID controller. The evaluation, conducted across three different trajectories, considered external disturbances and uncertainties in the dynamic parameters. The results demonstrate that the P-PID controller outperforms conventional PD and PID controllers, offering superior dynamic performance with reduced maximum overshoot, faster rise and settling time, minimized static error, and enhanced stability under challenging conditions.