Adaptive decentralized flocking control of multi-UAV circular formations based on vector fields and backstepping

Muslimov, Tagir; Munasypov, Rustem

doi:10.1016/j.isatra.2020.08.011

Cited by 43 publications

(35 citation statements)

References 46 publications

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“…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 Muslimov and Munasypov (2019) should be modified by adding the derivative signal as was done in Muslimov and Munasypov (2020). As direct differentiation can amplify noise and is undesirable, the derivative signal can be quite simply calculated analytically provided that the group members are communicating.…”

Section: Nonuniform Vector Field For Unmanned Aerial Vehicle Guidance In Standoff Trackingmentioning

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: Nonuniform Vector Field For Unmanned Aerial Vehicle 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 (Muslimov and Munasypov, 2020):…”

Section: Nonuniform Vector Field For Unmanned Aerial Vehicle Guidance In Standoff Trackingmentioning

confidence: 99%

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

Muslimov

Munasypov

2021

AEAT

Self Cite

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“…In [17], the target is assumed to follow a smooth trajectory specified a priori as the level curve of a known function. In [18], a backstepping-based controller is designed to follow a vector field to achieve a multiUAV formation on a circular orbit around a target while respecting the input constraints of the UAVs. Most of these approaches are designed towards tracking circular orbits.…”

Section: Related Workmentioning

confidence: 99%

“…3. The constraint on a and b given by (18). Imposing these constraints along with Lemma 1, we select the major and minor axes of the elliptical orbit as:…”

Section: Elliptical Orbit Computationmentioning

confidence: 99%

Aerial monitoring of slow moving convoys using elliptical orbits

Borkar

Sinha

2019

European Journal of Control

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“…UAVs have been widely utilized in military, civil, and other fields, such as military reconnaissance, aerial mapping, smart city traffic management, high-voltage line inspection, and polar environment detection [1]- [3]. Currently, advanced control topics, including coordinated control [4], swarm control [5], [6], obstacle avoidance [7], and path planning [8], are still attracting a large number of researchers to carry out theoretical analysis and experimental verification of fixedwing UAVs. To realize these advanced control technologies, it is very important that the UAVs have the ability to track a predefined trajectory or follow the desired path accurately.…”

Section: Introductionmentioning

confidence: 99%

Horizontal Nonlinear Path Following Guidance Law for a Small UAV With Parameter Optimized by NMPC

Chen¹,

Wang²,

Zeng

et al. 2021

IEEE Access

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In this study, the problem of guiding a small fixed-wing unmanned aerial vehicle (UAV) toward a predefined horizontal path is studied. A stable nonlinear guidance law, which is a function of the inertial positions and velocities of the UAV and the predefined path, is designed using Lyapunov stability arguments. The concept of the nonlinear model predictive control (NMPC) technique was applied to optimize a key parameter of the guidance law to improve the performance of the controller (PFC_NMPC), where the stability of the relative nonlinear system is maintained. The proposed method was verified in the MATLAB/Simulink environment to realize following the straight-line, square and circular paths. The pathfollowing performance of the proposed method is compared with those of the guidance laws with parameter fixed (PFC) or tuned by fuzzy logic (PFC_FL). With the predictive ability, the proposed method can make the UAV fly more on the desired square and circular paths than the other two methods, PFC and PFC_FL.

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Adaptive decentralized flocking control of multi-UAV circular formations based on vector fields and backstepping

Cited by 43 publications

References 46 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

Aerial monitoring of slow moving convoys using elliptical orbits

Horizontal Nonlinear Path Following Guidance Law for a Small UAV With Parameter Optimized by NMPC

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