Fuzzy Model Predictive Formation Maneuver Control of Multi-UAVs with Interval Output-Constrained

This paper considers the formation control of multiple unmanned aerial vehicles (UAVs), where the UAVs operate in dynamic environments with multiple obstacles and narrow (constrained) areas. A new fuzzy model predictive interval output-constrained maneuver formation control method for UAVs is propos...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2024-08, p.1-14
Main Authors: Du, Zhixu, Zhang, Hao, Wang, Zhuping, Yan, Huaicheng
Format: Article
Language:English
Subjects:
Aerospace electronics
Autonomous aerial vehicles
Collision avoidance
Formation control
Fuzzy logic systems
model predictive formation control
Nonlinear dynamical systems
obstacle avoidance
output-constrained
Predictive models
unmanned aerial vehicles
Vehicle dynamics
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Summary:This paper considers the formation control of multiple unmanned aerial vehicles (UAVs), where the UAVs operate in dynamic environments with multiple obstacles and narrow (constrained) areas. A new fuzzy model predictive interval output-constrained maneuver formation control method for UAVs is proposed. The interval output constraint algorithm utilizes the distance information to implement output constraints and formation changes, which are more practical but more challenging than the output constraint problem based on user-assigned settling time, especially in narrow areas. One of the distinctive advantages of the proposed output-constrained model predictive controller is that it can activate formation changes and output constraints for UAVs when passing through narrow space, and it can automatically restore the formation of UAV sand deactivate the output constraints when the UAVs are far away from the narrow space. Unlike most nonlinear model predictive control strategy where predictive control relies on the accurate underlying dynamical system, the paper introduces adaptive fuzzy updating law to receding horizon optimization algorithm to estimate and compensate unknown dynamics and external disturbances. Two potential field functions are designed to safely track in three-dimensional environments with obstacles. Finally, several examples are provided to illustrate the effectiveness of the proposed controller.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2024.3417903