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

Abstract

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.

Group movement of UAVs in environment with dynamic obstacles: a survey

“…Leaders can be strictly defined or appointed in real time depending on the external environment. The disadvantage of this method is that if the leader breaks or loses contact with him, the whole group breaks down (Muslimov and Munasypov, 2021).…”

“…the leader breaks or loses contact with him, the whole group breaks down (Muslimov and Munasypov, 2021). Decentralized control methods include collective, packing and swarm (Senotov et al, 2020;Cheng et al, 2013).…”

Optimum k-Nearest Neighbors for Heading Synchronization on a Swarm of UAVs under a Time-Evolving Communication Network

“…The authors claim that the quick response of the algorithm and high control precision, make this a suitable controller for engineering applications. On the other hand, in [ 28 ] the authors tackled the formation problem with a group of fixed-wing UAVs, employing fuzzy model reference adaptive control, to promote self-tuning of a vector-field-based consensus protocol. This combination allows target tracking with the group, while exhibiting an adaptive circular motion around the objective.…”

Distributed UAV swarm control framework with limited interaction for obstacle avoidance

“…Unmanned aerial vehicles (UAVs) are widely used in applications such as mapping, transportation, resource exploration and so on (Rakha and Gorodetsky, 2018; Xu et al , 2021; Li et al , 2022; Szczepanski and Purushothaman, 2021). In recent years, the UAV swarm and swarm behavior has attracted much more attentions (Pearce et al , 2014; Berlinger et al , 2021; Liang et al , 2021; Muslimov and Munasypov, 2021). Flocking animals in nature such as birds (Vásárhelyi et al , 2018) and fishes (Davidson et al , 2021) have been widely investigated.…”