Distributed Adaptive Fuzzy Formation Control of Uncertain Multiple Unmanned Aerial Vehicles With Actuator Faults and Switching Topologies

• Published in: IEEE transactions on fuzzy systems Vol. 31; no. 3; pp. 919 - 929
• Main Authors: Yu, Yajing, Guo, Jian, Chadli, Mohammed, Xiang, Zhengrong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Actuator faults; Actuators; Adaptation models; Adaptive control; adaptive fuzzy control; Attitudes; Automatic; Autonomous aerial vehicles; Control systems design; Controllers; Engineering Sciences; Formation control; Fuzzy control; multiple unmanned aerial vehicles (UAVs); Sliding mode control; Subsystems; Switches; Switching; switching topologies; Topology; Tracking errors; Unmanned aerial vehicles; Unmanned helicopters; unmodeled dynamics; Vehicle dynamics; Actuators; Adaptation models; Multiple unmanned aerial vehicles; Switching topologies; Unmodeled dynamics; Actuator faults; Switches; Adaptive fuzzy control; Autonomous aerial vehicles; Formation control; Topology; Vehicle dynamics
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2022.3193440

This article investigates a distributed fuzzy adaptive formation control for quadrotor multiple unmanned aerial vehicles (UAVs) under unmodeled dynamics and switching topologies. The UAVs dynamics model is described by the Newton-Euler formula, and the actuator faults are considered in the system model in the form of multiplicative factors and additive factors. Due to the underactuated characteristics of the UAVs, two objective attitude commands are generated by designing a virtual control signal, which are transmitted to the attitude subsystem, and then the position controller is solved. By constructing a distributed communication mechanism between UAVs, an adaptive formation control strategy is proposed, which can enable UAVs to update their position and speed online according to their neighbor information, and then achieve the required formation. In addition, a fuzzy adaptive sliding mode controller is designed to ensure that the tracking errors of UAVs converge to the neighborhood of the origin. Finally, the simulation results verify the effectiveness of the proposed control strategy.