Distributed Observer-Based Leader Following Consensus Tracking Protocol for a Swarm of Drones

Zaidi, Adeel; Kazim, Muhammad; Weng, Rui; Wang, Dongzhe; Zhang, Xu

doi:10.1007/s10846-021-01401-6

Cited by 21 publications

(12 citation statements)

References 31 publications

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“…This subsection recalls some essential results and basic definitions from the algebraic graph theory [28] that will be utilized to express the inter-drone connectivity in the form of matrices and graphs.…”

Section: B Algebraic Graph Theorymentioning

confidence: 99%

Adaptive Active Disturbance Rejection Control for Rendezvous of a Swarm of Drones

et al. 2022

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This paper addresses the time-varying rendezvous problem of a swarm of drones with the leader-follower consensus hierarchy. Each drone in the swarm is under the influence of external disturbances in the form of wind gusts. To control the swarm of perturbed drones, this paper proposes a fully distributed adaptive leader-following time-varying disturbance rejection pinning control for the rendezvous of drones using the framework of the extended state observer that depends on the state estimates of the neighboring agents rather than the actual states. The communication topology within the swarm is modelled using algebraic graph theory. Unlike most of the literature in which the leader agent act as a virtual reference generator, an active leader agent with non-zero control input is considered. The states of the leader drone are only available to a subset of the graph. The communication topology between leader-follower is directed while that between follower-follower is undirected. Based on the relative output information between the neighboring drones, the extended state observer estimates the local states and disturbances of each drone and the adaptive control actively compensates for the external disturbances simultaneously. Finally, it is shown analytically as well as in simulations that the time-varying rendezvous can be achieved by the proposed algorithm effectively.

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Section: B Algebraic Graph Theorymentioning

confidence: 99%

Adaptive Active Disturbance Rejection Control for Rendezvous of a Swarm of Drones

et al. 2022

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“…Even when multiple UAVs can be controlled, these programs usually do not provide real-time synchronized control and independent flight control. Research is underway to solve this problem and enable a large number of UAVs to fly in groups [18,19]; moreover, research has been actively conducted for the cluster flight of a large number of UAVs in agriculture. Ju and Son used seven measurement indices (total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio) to evaluate the performance of single and multiple UAVs during agricultural work, and proved that the multi-UAV system is more efficient than the single UAV system (total flight time improved by 18.1%, battery consumption reduced by 59.8%, coverage area increased by 200%, etc.)…”

Section: Introductionmentioning

confidence: 99%

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Lee

Shin

Thomasson

et al. 2022

Sensors

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Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.

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“…Since, the dynamical model of quadrotor is extremely vulnerable to external disturbances [4], various control strategies have been investigated and proposed for a single quadrotor such as backstepping control [5], sliding mode control [6], proportional-integral derivative (PID) control [7], and linear quadratic regulator (LQR) control [8] etc. More challenging task is to deal with the swarm of drones and recently some methods are proposed such as containment control for multiple quadrotors [9] and leader-follower approach with robust observer-based control for dynamic trajectory tracking [10]. Other various conventional and modern control techniques are presented in [11] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

A Robust Backstepping Sliding Mode Controller with Chattering-Free Strategy for a Swarm of Drones

Zaidi

Kazim

Wang

2022

J. Phys.: Conf. Ser.

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This paper presents a robust controller for a swarm of drones that are subjected to external disturbances. Integrating a fuzzy-based system in the sliding mode control technique, a chattering-free controller is proposed for the trajectory tracking of a drone. Furthermore, the formation controller for a group of drones is developed using the Backstepping control design technique. The integration of a fuzzy rule-based system in the sliding mode tracking controller enables overcoming the inherent chattering problem due to discontinuous switching function in the sliding mode control technique and provides smooth and chattering-free control signals. The asymptotic stability of the overall system is shown using the Lyapunov stability theory. Finally, the control design performance is evaluated using numerical simulations.

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Distributed Observer-Based Leader Following Consensus Tracking Protocol for a Swarm of Drones

Cited by 21 publications

References 31 publications

Adaptive Active Disturbance Rejection Control for Rendezvous of a Swarm of Drones

Adaptive Active Disturbance Rejection Control for Rendezvous of a Swarm of Drones

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

A Robust Backstepping Sliding Mode Controller with Chattering-Free Strategy for a Swarm of Drones

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