Evaluation of control techniques for quadcopter UAV attitude tracking

ABSTRACT

This paper evaluates the performance of six controllers used for the attitude tracking of the quadcopter. The evaluation is done by testing the tracking performance and robustness of each controller with respect to unknown dynamics, disturbances, gain variations, and noise. These controllers include the well-known Proportional-Integral-Derivative (PID) controller to establish a baseline, the Linear Active Disturbance Rejection Controller (LADRC), the first-order Sliding Mode Controller (SMC), the second-order Super-Twisting SMC (STSMC), the Backstepping Controller (BSC), and synergetic controller. To ensure a fair and systematic evaluation, the parameters of each control method were optimised using a Particle Swarm Optimizer (PSO), incorporating a penalty term to maintain realistic control signals while minimising error. The paper details the control techniques used and describes the optimisation process. The results suggest the superiority of LADRC over the other controllers. In the conclusion section, the paper presents several prospective strategies aimed at enhancing the discussed control techniques.

KEYWORDS:

• Quadcopter control
• sliding mode control
• super-twisting sliding mode control
• synergetic control
• active disturbance rejection control
• backstepping control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 A system representative point is a point in the state space that represents the current state of the system

Additional information

Notes on contributors

Saif Sinan

Saif Sinan is currently pursuing his Ph.D. at École de Technologie Supérieure. His research primarily centres around nonlinear control, multi-robotic system collaboration, with an additional emphasis on quadcopter control and flight systems.

Mahmoud Bakr

Mahmoud Bakr is currently pursuing his master's degree in Mechanical Engineering at the University of British Columbia. His research interest lies in AI applications in robotics.

Raouf Fareh

Raouf Fareh received his Ph.D. degree from École de Technologie Supérieure in 2013. He is now an Associate Professor at the University of Sharjah, specialising in control and path planning for robotic systems.

Maarouf Saad

Maarouf Saad obtained his Ph.D. from McGill University in 1988 and joined École de Technologie Supérieure in 1987. His research is dedicated to nonlinear control and optimisation applied to robotics and autonomous systems.

Maamar Bettayeb

Maamar Bettayeb earned his Ph.D. from the University of Southern California in 1981. He currently serves as the Vice Chancellor for Research and Graduate Studies at the University of Sharjah. His research interests encompass control and soft computing.