Decentralized coordinated control for a group of aircraft

Bukov, V. N.; Bronnikov, A. M.; Selvesyuk, N. I.

doi:10.1243/09544100jaero671

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Many papers dwell upon decentralized UAV interaction; however, they made assumptions absent herein. Thus, Bukov et al (2010) studied a similar configuration with evidence from a dual-UAV leader-follower setup; Fathian et al (2018) ignored the input constraints in their proof of convergence. Linear consensus for UAVs was addressed by Kolaric et al (2018), Jia et al (2019) and Liao et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC

Muslimov

Munasypov

2021

AEAT

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Purpose This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain. Design/methodology/approach This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). Findings Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models. Research limitations/implications Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless. Originality/value In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

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Section: Introductionmentioning

confidence: 99%

Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC

Muslimov

Munasypov

2021

AEAT

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“…Many papers dwell upon decentralized UAV interaction; however, they made assumptions absent herein. Thus, Bukov et al [17] studied a similar configuration with evidence from a dual-UAV leader-follower setup; Fathian et al [18] ignored the input constraints in their proof of convergence. Linear consensus for UAVs was addressed by Kolaric et al [19], Jia et al [20], Liao et al [21], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Cooperative Target Tracking via Consensus-based Guidance Vector Fields and Fuzzy MRAC

Muslimov¹,

Munasypov²

2021

Preprint

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This paper proposes a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more ac-curately. Thus, the controls should ensure that: (1) the UAV swarm follows a moving circular path whose center is the target while also attaining and maintain-ing a circular formation of a specific geometric shape; (2) the formation control systems is capable of self-tuning since the UAV dynamics is uncertain. In con-trast to known studies, this one uses Lyapunov vector guidance fields that are di-rection- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus. This paper considers two interaction architectures: an open chain where each UAV only interacts with its neighbors; and cooperative leader, where the leading UAV is involved in attain-ing the formation. Using open chain decentralized architecture allows to have an unlimited number of aircraft in a group, which is in line with the swarm behavior concept. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models.<br>

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Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping

Muslimov

Munasypov

2021

Aerospace Science and Technology

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Decentralized coordinated control for a group of aircraft

Cited by 4 publications

References 13 publications

Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC

Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC

Multi-UAV Cooperative Target Tracking via Consensus-based Guidance Vector Fields and Fuzzy MRAC

Consensus-based cooperative control of parallel fixed-wing UAV formations via adaptive backstepping

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