<p>This paper proposes a novel distributed fixed-time adaptive control scheme for cooperative tracking of quadrotor unmanned aerial vehicle (UAV) formations under communication-constrained conditions. Unlike traditional methods that rely on global information or asymptotic convergence, the proposed approach guarantees fixed-time convergence of both state estimation and control within a predefined time bound. First, a distributed fixed-time observer is designed to reconstruct the virtual leader’s position and velocity using only local information. Based on this, a fuzzy adaptive nonsingular terminal sliding mode controller is developed to ensure robust fixed-time convergence of the position tracking. Furthermore, a computationally efficient indirect fuzzy identification mechanism is introduced, which requires only a single adaptive parameter to estimate the upper bound of the lumped uncertainties, thereby significantly reducing the online computational burden. Simulation results demonstrate the effectiveness, fast convergence, and robustness of the proposed method, highlighting its potential for UAV formation control in communication-constrained environments.</p>

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Distributed fixed time adaptive cooperative tracking control for quadrotor unmanned aerial vehicle

  • Jinghong Zhou,
  • Xihuizi Liang,
  • Min Shi,
  • Zitai Liu

摘要

This paper proposes a novel distributed fixed-time adaptive control scheme for cooperative tracking of quadrotor unmanned aerial vehicle (UAV) formations under communication-constrained conditions. Unlike traditional methods that rely on global information or asymptotic convergence, the proposed approach guarantees fixed-time convergence of both state estimation and control within a predefined time bound. First, a distributed fixed-time observer is designed to reconstruct the virtual leader’s position and velocity using only local information. Based on this, a fuzzy adaptive nonsingular terminal sliding mode controller is developed to ensure robust fixed-time convergence of the position tracking. Furthermore, a computationally efficient indirect fuzzy identification mechanism is introduced, which requires only a single adaptive parameter to estimate the upper bound of the lumped uncertainties, thereby significantly reducing the online computational burden. Simulation results demonstrate the effectiveness, fast convergence, and robustness of the proposed method, highlighting its potential for UAV formation control in communication-constrained environments.