The Approach to Managing a Group of UAVs as a System with Distributed Parameters

Milyakov, Denis; Меркулов, В. И.

doi:10.1016/j.procs.2019.02.009

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…It was originally proposed in [27,28] for mobile robots as linear agents. UAVs are expected to interact by the rules presented in (5) and (6). Let H is a matrix that specifies the agents for agent-to-agent distance measurements, defined as follows:…”

Section: Decentralized and Centralized Uav Interaction Architecturementioning

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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Section: Decentralized and Centralized Uav Interaction Architecturementioning

confidence: 99%

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

Muslimov¹,

Munasypov²

2021

Preprint

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“…Decentralized control of autonomous robots is one of the more complex yet effective approaches. Thus, many papers cover decentralized control applications in ground-based robots [1][2][3] and autonomous unmanned aerial vehicles (UAVs) alike [4,5]. Most papers cover decentralized control of rotary-wing UAV groups, mainly quadcopter formations [6,7].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Target Encirclement by a UAV Formation

Muslimov

2023

Intels’22

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“…Unmanned aerial vehicle (UAV) formations have a wide range of applications. As many researchers note, using coordinated groups of autonomous mobile robots (Darintsev et al , 2019; Ivanov, 2019; Zenkevich et al , 2020) including UAVs (Alonso-Mora et al , 2019; Amelin et al ., 2019; Guzey et al , 2019; Hock and Schoellig, 2019; Milyakov and Merkulov, 2019) can address a variety of complex problems that a single aircraft would not be able to solve. Besides, some standard autonomous fixed-wing UAV applications do benefit from using UAV groups; for instance, a tight formation consumes less resources (Liu and Zhou, 2018; Zhang and Liu, 2018).…”

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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The Approach to Managing a Group of UAVs as a System with Distributed Parameters

Cited by 4 publications

References 2 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

Particle Swarm Optimization for Target Encirclement by a UAV Formation

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

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