Sensitivity analysis of intelligent active force control applied to a quadrotor system

• Main Authors: Abdelmaksoud, Sherif I., Mailah, Musa, Abdallah, Ayman M.
• Format: Conference or Workshop Item
• Published: 2022
• Subjects: Q Science (General); QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/101084/; http://dx.doi.org/10.1007/978-3-030-85990-9_14

This paper introduces a hybrid controller to be employed for stabilizing a quadrotor, as an example of rotor unmanned aerial vehicle (UAV) systems, and efficiently repelling the applied perturbations during trajectory tracking in a complex environment via a simulation study. In this study, Newton-Euler’s method was used to find the equations of motion for the dynamic model of the quadrotor system taking into account the effects of aerodynamic, gyroscopic, perturbation, and friction. The proposed control structure was comprised of a proportional-integral-derivative (PID) control scheme and an innovative control technique known as active force control (AFC). The AFC was tuned intelligently using artificial intelligence (AI)-based approach, namely, iterative learning algorithm (ILA), to be defined as intelligent active force control (IAFC), and the proposed strategy was identified as (PID-ILAFC) scheme. To evaluate the feasibility of the proposed control scheme, a sinusoidal wave disturbance was introduced as an example of external perturbation. In this study, a sensitivity analysis was also performed considering the variance of the estimated inertia value, model uncertainty, and the AFC output signal percentage to achieve the best possible performance. The outcomes show the efficacy of the IAFC-based strategy in expelling off the applied perturbations and uncertainties in comparison with the PID controller. The results also indicate the importance of the accurate selection of the estimated inertia value to enhance the effectiveness of the AFC approach and the significance of using the full output of the AFC signal to obtain the best performance.