Swarm Coordination and Trajectory Tracking in Quadrotor UAVs Using Fractional-Order PID Control Strategy

Quadrotor unmanned aerial vehicles (QUAVs) are inherently underactuated, which makes it challenging to accomplish precision control and trajectory tracking, particularly in flight circumstances that are intricate. This is especially true in situations where flights are complicated. For the goal of this investigation, a sophisticated Fractional-Order PID (FOPID) controller is presented. This controller will be superior in terms of performance to both conventional PID controllers and Integral State Feedback Algorithm (ISFA) controllers. The adoption of the FOPID design results in improvement in tracking accuracy, decreases in overshoot and steady-state error, and better resistance to disturbances and system uncertainties. All of these benefits are obtained through the implementation of the design. The controller demonstrates superior transient and steady-state performance, as demonstrated by simulations carried out in MATLAB/Simulink and validations carried out through experiments utilising two different scenarios, such as helical trajectory tracking and circular swarm formation, and two different hardwares, such as QDrones by Quansar and CoDrones by Robolink. The results proved that the FOPID technique demonstrates a high degree of adaptability and scalability, which makes it an ideal choice for swarm-based missions as well as real-world applications such as environmental monitoring. This is accomplished through its integration with a helical trajectory tracking and circular swarm formation framework that is implemented across two different hardwares. FOPID is a strategy that is both effective and feasible for the regulation of QUAVs of the future generation, as demonstrated by the outcomes of this study. Note to Practitioners—This paper presents a fractional-order PID controller for the precise helical trajectory tracking of quadrotor UAVs (QUAVs) in both alone and in case of QUAV swarm settings. The FOPID controller enhances robustness, reduces overshoots, and optimises reaction time compared to conventional PID controllers while managing underactuated dynamics and disturbance susceptibility of QUAVs. This will be particularly useful for practitioners in environmental monitoring, search and rescue, and swarm robotics, safely performing tasks in dynamic, densely packed environments. The proposed controller will improve the efficiency and increase the scale of operations for UAVs in a wide range of applications with low tuning requirements and practical applicability.