UAV Formation Flight Using Non-Uniform Vector Field and Fuzzy Self-Tuning PD-Control

Muslimov, Tagir; Munasypov, Rustem

doi:10.1109/rusautocon.2018.8501769

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Then, according to Muslimov and Munasypov (2018), the following fuzzy rules can apply: IF y is positive AND _ y is positive THEN W is negative big IF y is negative AND _ y is negative THEN W is positive big IF y is positive AND _ y is negative THEN W is zero IF y is negative AND _ y is positive THEN W is zero IF y is zero AND _ y is positive THEN W is negative IF y is zero AND _ y is negative THEN W is positive Similar reasoning applies when tuning the parameters for speed control laws (9).…”

Section: Fuzzy Model Reference Adaptive Control For Selftuning In Target Trackingmentioning

confidence: 99%

“…This section describes how fuzzy model reference adaptive control could enable self-tuning for these coefficients. A similar approach was used in Muslimov and Munasypov (2018) to self-tune the parameters for a UAV group following a rectilinear path. The strategy essentially boils to adding the second-order unicycle model (1) with input constraints (2) to each UAV, which will use the data from other UAVs to calculate the output.…”

Section: Adaptive Unmanned Aerial Vehicle Formation Control Strategy For Tracking a Moving Targetmentioning

confidence: 99%

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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: Fuzzy Model Reference Adaptive Control For Selftuning In Target Trackingmentioning

confidence: 99%

Section: Adaptive Unmanned Aerial Vehicle Formation Control Strategy For Tracking a Moving Targetmentioning

confidence: 99%

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

Muslimov

Munasypov

2021

AEAT

Self Cite

View full text Add to dashboard Cite

show abstract

“…This section describes how fuzzy model reference adaptive control could enable selftuning for these coefficients. A similar approach was used in [29] to self-tune the parameters for a UAV group following a rectilinear path. The strategy essentially boils to adding the second-order unicycle model (1) with input constraints (2) to each UAV, which will use the data from other UAVs to calculate the output.…”

Section: Fuzzy Model Reference Adaptive Control For Self-tuning In Tamentioning

confidence: 99%

“…Rules for the fuzzy controller are chosen on the basis of the fuzzy Lyapunov functions [30]. Unlike in [29], the authors' approach implies making rules for the error derivative signal as well. Another difference is that the authors' approach involves tuning not only for the distance-to-final-path error coefficient, but also for the derivative of the same error.…”

Section: Fig 1 Fuzzy Model Reference Adaptive Control For a Uav Formentioning

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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“…Numerical simulations are presented to validate the theoretical scheme. Muslimov and Munasypov 10 presented a new three-dimensional formation flight control algorithm for fixed-wing UAVs based on non-uniform guidance vector field. Consensus-based model of living organisms motor neural network was used for generalized law of UAVs interaction, and the adaptive loop stability is provided with fuzzy Lyapunov synthesis.…”

Section: Introductionmentioning

confidence: 99%

A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles

Ollervides-Vazquez

Rojo-Rodriguez

Garcia‐Salazar

et al. 2020

International Journal of Micro Air Vehicles

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This paper presents an algorithm based on fuzzy theory for the formation flight of the multi-quadrotors. For this purpose, the mathematical model of N-quadrotor unmanned aerial vehicles is presented using the Newton-Euler formulation. The strategy of the formation flight is based on a structure composed by a sectorial fuzzy controller and the linear systems whose state variables are the position and velocity of the ith quadrotor. The stability analysis is described as a generalized form for N-quadrotor unmanned aerial vehicles and it is based on the Lyapunov theory. This analysis demonstrates that the closed-loop system is globally asymptotically stable so that the quadrotors unmanned aerial vehicles reach the consensus. Numerical simulation demonstrates the robustness of the proposed scheme for the formation flight even in the presence of disturbances. Finally, experimental results show the feasibility of the proposed algorithm for the formation flight of multiple unmanned aerial vehicles.

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UAV Formation Flight Using Non-Uniform Vector Field and Fuzzy Self-Tuning PD-Control

Cited by 7 publications

References 8 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

A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles

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