Coordinated UAV Standoff Tracking of Moving Target based on Lyapunov Vector Fields

Muslimov, Tagir; Munasypov, Rustem

doi:10.36227/techrxiv.12122943

Cited by 1 publication

(5 citation statements)

References 2 publications

(3 reference statements)

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“…[24] provide the standard notation. This research uses an open-chain interaction topology similar to that in [15,25]. In such cases, the system can be deemed decentralized as each UAV is only communicating with its neighbors.…”

Section: Multi-uav System Modelmentioning

confidence: 99%

“…Let i be the set of all agents. Their interaction architecture can be described by a strongly connected graph as in [15,25]:…”

Section: Multi-uav System Modelmentioning

confidence: 99%

“…The control strategy is based on using a direction-and magnitude-nonuniform path following vector field [15,25]. To use this method, plot a vector field in each point of the UAV flight space to set the heading angle and the speed.…”

Section: Nonuniform Vector Field For Uav Guidance In Standoff Trackingmentioning

confidence: 99%

“…To use this method, plot a vector field in each point of the UAV flight space to set the heading angle and the speed. However, the control laws obtained in [15] should be modified by adding the derivative signal as was done in [25].…”

Section: Nonuniform Vector Field For Uav Guidance In Standoff Trackingmentioning

confidence: 99%

“…In case of no communication, Kalman filtering can be applied to estimate the states. The proposed modified UAV formation speed control law is thus written as follows [25]:…”

Section: Nonuniform Vector Field For Uav Guidance In Standoff Trackingmentioning

confidence: 99%

See 4 more Smart Citations

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: Multi-uav System Modelmentioning

confidence: 99%

“…Let i be the set of all agents. Their interaction architecture can be described by a strongly connected graph as in [15,25]:…”

Section: Multi-uav System Modelmentioning

confidence: 99%

Section: Nonuniform Vector Field For Uav Guidance In Standoff Trackingmentioning

confidence: 99%

Section: Nonuniform Vector Field For Uav Guidance In Standoff Trackingmentioning

confidence: 99%

“…In case of no communication, Kalman filtering can be applied to estimate the states. The proposed modified UAV formation speed control law is thus written as follows [25]:…”

Section: Nonuniform Vector Field For Uav Guidance In Standoff Trackingmentioning

confidence: 99%

See 3 more Smart Citations

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

Muslimov¹,

Munasypov²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Coordinated UAV Standoff Tracking of Moving Target based on Lyapunov Vector Fields

Abstract: Accepted at "Nonlinearity, Information and Robotics 2020" Conference<br>

Cited by 1 publication

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

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