Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping

The paper proposes a novel approach to control fixed-wing unmanned aerial vehicle (UAV) swarm to start and keep flying in a parallel formation of a specific geometry. The distinctive feature of this method is that it is infinitely scalable thanks to synthesizing decentralized cooperative control laws that are globally asymptotically stable. These laws use the architecture of leaderless consensus, in which each UAV communicates with the adjacent UAVs only. Unlike standard linear consensus, these formation controllers consider both the nonholonomic dynamics of fixed-wing UAV and the input constraints for the autopilot-UAV system, which enables the aircraft to take any initial position before the algorithms start running. Together, coordinated control laws generate vector field for cooperative rectilinear path following, a field that is non-uniform in direction and magnitude. The research further applies backstepping techniques to generate control inputs for a more realistic autopilot-UAV model, which is adjusted in view of the fact that the autopilot response to speed- and path-related input is naturally first-order. We have implemented adaptive parametric self-tuning in autopilot-UAV models, as flight can affect the dynamics of any UAV. As a result, each UAV takes its own spot in a parallel formation, whereby each aircraft's speed asymptotically tends to the final cruising speed of the formation while the course angle tends to final course angle, which helps keep the formation stable and its geometry precise. The effectiveness of the proposed approach has been tested in MATLAB/Simulink using six-DOF 12-state non-linear fixed-wing UAV models. (C) 2020 Elsevier Masson SAS. All rights reserved.

Automated summary

The paper introduces a novel approach for controlling a swarm of fixed-wing UAVs to fly in parallel formation, using cooperative control laws and backstepping techniques to ensure stability and precision in flight.