Adaptation to Unknown Leader Velocity in Vector-Field UAV Formation

Baldi, Simone; Sun, Danping; Zhou, Guopeng; Liu, Di

doi:10.1109/taes.2021.3103583

Cited by 23 publications

(6 citation statements)

References 51 publications

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“…Proof. It has been proved in [40] that the geometric convex combination g Gi has the kinematics as (12). In the following, we prove that the velocity convex combination ξ Gi satisfies the nonholonomic constraints.…”

Section: Communication Topology Transformationmentioning

confidence: 73%

“…Therefore, based on Lemma 1, the virtual parent node g Gi is described by the kinematics (12) with the nonholonomic constraints (14), which indicates that the virtual parent node has the same model as any of fixed-wing UAV in the swarm. Furthermore, given the fact that there is no cycle in the communication graph, then for each follower g i , the motion of its virtual parent node g Gi is independent of g i 's own motion.…”

Section: Communication Topology Transformationmentioning

confidence: 99%

“…Among various coordination tasks, the most common one for fixed-wing UAVs is the formation control [6], which aims at driving multiple UAVs to achieve the predefined constraints regarding their states, generally interpreted as the desired geometric formation patterns. Plenty of existing articles have investigated the formation control of fixed-wing UAVs with diverse approaches, such as leader-follower structure [7], [8], consensus-based method [9], [10], guiding vector field [11], [12], graph rigidity [13], [14]. Additionally, several papers further focus on the formation reconfiguration [15], [16] and maneuverability [17], [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Roto-Translation Invariant Formation of Fixed-Wing UAVs in 3D: Feasibility and Control

He¹,

Li²,

Wang³

et al. 2023

Preprint

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This paper investigates the formation of fixed-wing UAVs in 3D, which communicate via a directed acyclic graph. Different from common formation problems, we consider the roto-translation invariant (RTI) formation, where the "rototranslation" refers to a rigid-body motion obtained by composing rotation and translation. Besides, the fixed-wing UAV is modelled by a 3-D nonholonomic constrained rigid body instead of a particle agent. The main results of this paper include proposing the formation feasibility conditions and designing the formation controller. Firstly, we define the RTI formation and propose the conditions to guarantee that the formations are feasible for the fixed-wing UAVs under the nonholonomic and input saturation constraints. Secondly, given feasible formations, we design a formation controller by introducing a virtual leader and employing the compensation of rotation, followed by proving the stability of the closed-loop system. Finally, simulation examples are presented to verify the theoretical results.al with the motion coupling among multiple robots. Finally, numerical simulations are conducted to verify the theoretical results.

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Section: Communication Topology Transformationmentioning

confidence: 73%

Section: Communication Topology Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Roto-Translation Invariant Formation of Fixed-Wing UAVs in 3D: Feasibility and Control

He¹,

Li²,

Wang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Adaptation of UAVs to different wind conditions has also been the subject of several studies, usually relying on compensating for the offset position error induced by the wind disturbance [20]- [22]. Adaptation in formations of UAVs was studied in [23], [24]. These and other works show the importance of overcoming the linear control implementation of off-the-shelf autopilots.…”

Section: Related Workmentioning

confidence: 99%

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Liu

Xia

Baldi

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

Self Cite

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The operation of Unmanned Aerial Vehicles (UAVs) is often subject to state-dependent alterations and unstructured uncertainty factors, such as unmodelled dynamics, environmental weather disturbances, aerodynamics gradients, or changes in inertia and mass due to payloads. While a large number of autopilot solutions have been proposed to operate UAVs, none of these solutions is able to counteract the effects of state-dependent and unstructured uncertainties online by parameter estimation and adaptive control techniques. This work presents a systematic integration of adaptive control into ArduPilot, a popular open-source autopilot suite maintained by a large community of UAV developers. Adaptation features are embedded in the ArduPilot control structure without altering the original architecture, to allow users to use the autopilot suite as usual. Tests show that the proposed adaptive ArduPilot provides consistent improved performance in several uncertain flight conditions. The source code of the proposed adaptive ArduPilot is released at https://github.com/ Friend-Peng/Adaptive-ArduPilot-Autopilot.

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“…A new adaptive formation control method was proposed for UAVs with limited leader information and communication. The method was extended to replace the leader with adjacent UAVs, where the leader can convey location and direction information [13].…”

Section: Introductionmentioning

confidence: 99%

Connectivity-Maintenance UAV Formation Control in Complex Environment

Zhu

et al. 2023

Drones

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Cooperative formation control is the research basis for various tasks in the multi-UAV network. However, in a complex environment with different interference sources and obstacles, it is difficult for multiple UAVs to maintain their connectivity while avoiding obstacles. In this paper, a Connectivity-Maintenance UAV Formation Control (CMUFC) algorithm is proposed to help multi-UAV networks maintain their communication connectivity by changing the formation topology adaptively under interference and reconstructing the broken communication topology of a multi-UAV network. Furthermore, through the speed-based artificial potential field (SAPF), this algorithm helps the multi-UAV formation to avoid various obstacles. Simulation results verify that the CMUFC algorithm is capable of forming, maintaining, and reconstructing multi-UAV formation in complex environments.

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Adaptation to Unknown Leader Velocity in Vector-Field UAV Formation

Cited by 23 publications

References 51 publications

Roto-Translation Invariant Formation of Fixed-Wing UAVs in 3D: Feasibility and Control

Roto-Translation Invariant Formation of Fixed-Wing UAVs in 3D: Feasibility and Control

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Connectivity-Maintenance UAV Formation Control in Complex Environment

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