Prescribed-Time Formation Tracking Control of Fixed-Wing UAVs with Disturbance and Failures
Gongxian Lou, Maolong Lv
Air Force Engineering University
内容与影响
This paper proposes a novel prescribed-time formation tracking control farmework of multi-fixed-wing UAVs under external disturbance and actuator failures. As the complexity of aerial missions intensifies, achieving precise position and attitude tracking within a user-defined upper bound of settling time becomes a paramount challenge for intelligent swarm systems. Unlike traditional finite or fixed-time methods, where convergence depends on initial states or suffers from conservative estimation, the proposed approach ensures stability within a prescribed time independent of initial conditions. A key innovation is the introduction of a piecewise reference convergence differential function. This mechanism eliminates the need for state transitions, thereby reducing computational complexity while ensuring smooth tracking without control surface chattering across the entire mission. Additionally, a prescribed-time sliding mode disturbance observer is developed to provide precise and timely compensation for external disturbances and actuator faults. Rigorous Lyapunov analysis proves that all closed-loop signals are bounded and the tracking errors converge to a small neighborhood of zero within the predefined time. Numerical simulations demonstrate that, under time-varying disturbances and actuator faults, the disturbance estimation errors converge within 4 s, while both attitude and velocity tracking errors converge within 6 s, achieving fast transient response and high tracking accuracy. The UAV swarm successfully maintains the desired formation during aggressive maneuvers, including speed variations, climbing, and diving. These results verify that the proposed method provides a computationally efficient, robust, and high-precision solution for time-critical formation control of fixed-wing UAV swarms under complex uncertainties.
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计算机 / AIDistributed Control Multi-Agent Systems
Adaptive Control of Nonlinear Systems · Spacecraft Dynamics and Control