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

Area of Science:

• Robotics and Control Systems
• Aerospace Engineering
• Artificial Intelligence

Background:

• Cooperative tracking of quadrotor unmanned aerial vehicle (UAV) formations is crucial for many applications.
• Existing methods often require global information or achieve only asymptotic convergence, limiting practical deployment.
• Communication constraints in UAV formations pose significant challenges for traditional control strategies.

Purpose of the Study:

• To develop a novel distributed fixed-time adaptive control scheme for quadrotor UAV formations.
• To achieve cooperative tracking under communication-constrained conditions with guaranteed fixed-time convergence.
• To enhance robustness and reduce computational load in UAV formation control.

Main Methods:

• Design of a distributed fixed-time observer for state estimation using local information.
• Development of a fuzzy adaptive nonsingular terminal sliding mode controller for robust position tracking.
• Introduction of an efficient indirect fuzzy identification mechanism for uncertainty estimation with a single adaptive parameter.

Main Results:

• The proposed scheme guarantees fixed-time convergence for state estimation and control.
• Achieved robust and fast position tracking convergence in simulations.
• Demonstrated significant reduction in online computational burden through an efficient identification mechanism.

Conclusions:

• The novel distributed fixed-time adaptive control scheme is effective for quadrotor UAV formation cooperative tracking.
• The method offers improved performance, robustness, and efficiency compared to traditional approaches, especially under communication constraints.
• Highlights the potential for practical implementation in real-world UAV formation control scenarios.