Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

Wang, Yingxun; Tian, Zhang; Cai, Zhihao; Zhao, Jiang; Wu, Kun

doi:10.1016/j.cja.2020.04.028

Cited by 52 publications

(30 citation statements)

References 29 publications

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“…In [8], for longitudinal synchronization tracking of multiple UAVs, a distributed cooperative fault-tolerant controller was proposed in the presence of input saturation. Based on swarm intelligence, [9] proposed a distributed model predictive approach for coordination control of multiple UAVs. In [10], with path-following vector fields, the UAV group achieves a circular motion around the target, while rendering the course angles and speeds to converge to the vector field-specified values.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is worth mentioning that all aforementioned methods [7]- [10] are performed based on the assumption that either the UAV dynamics can be simplified to be twodegree models [9]- [10], or only attitude/longitudinal models are taken into account [7]- [8]. Such a model simplification unavoidably lowers the system description precision.…”

Section: Introductionmentioning

confidence: 99%

“…This technique has been successfully applied to UAV systems. Based on distributed model predictive control scheme, the event-triggered mechanism was developed in [9], [12]- [13] Airspeed Controller…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Zhang

Sun

et al. 2022

IEEE Trans. Veh. Technol.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Zhang

Sun

et al. 2022

IEEE Trans. Veh. Technol.

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“…Fehmi Burcin Ozsoydan [ 50 ] investigated the effects of dominant wolves and made modifications to the GWO based on the variations of dominant wolves. Wang et al [ 51 ] provided a GWO variant based on chaotic initialization. Despite the effectiveness of these adjustments, the changes in the hierarchy structure or population structure can affect the information flow mechanism of the GWO.…”

Section: Introductionmentioning

confidence: 99%

Dimensional Learning Strategy-Based Grey Wolf Optimizer for Solving the Global Optimization Problem

Liu

Wang

Zhou

2022

Computational Intelligence and Neuroscience

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Grey wolf optimizer (GWO) is an up-to-date nature-inspired optimization algorithm which has been used for solving many of the real-world applications since it was proposed. In the standard GWO, individuals are guided by the three dominant wolves alpha, beta, and delta in the leading hierarchy of the swarm. These three wolves provide their information about the potential locations of the global optimum in the search space. This learning mechanism is easy to implement. However, when the three wolves are in conflicting directions, an individual may not obtain better knowledge to update its position. To improve the utilization of the population knowledge, in this paper, we proposed a grey wolf optimizer based on the dimensional learning strategy (DLGWO). In the DLGWO, the three dominant wolves construct an exemplar wolf through the dimensional learning strategy (DLS) to guide the grey wolves in the swarm. Thereafter, to reinforce the exploration ability of the algorithm, the Levy flight is also utilized in the proposed method. 23 classic benchmark functions and engineering problems are used to test the effectiveness of the proposed method against the standard GWO, variants of the GWO, and other metaheuristic algorithms. The experimental results show that the proposed DLGWO has good performance in solving the global optimization problems.

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“…Furthermore, ground-based forest fire suppression/management is time-consuming and costly, as it requires ground staff and aircraft pilots. two years, researchers have employed the GWO algorithm to solve localization [14], path planning [15], [16], topology control [17], [18], and clustering [19] problems in different types of networks.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

Arafat

Moh

2021

IEEE Access

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In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.INDEX TERMS Bio-inspired algorithm, cluster head, clustering, energy efficiency, gray wolf optimization, localization, network lifetime, routing protocol, unmanned aerial vehicle network.

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Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

Cited by 52 publications

References 29 publications

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Event-Triggered Adaptive Fault-Tolerant Synchronization Tracking Control for Multiple 6-DOF Fixed-Wing UAVs

Dimensional Learning Strategy-Based Grey Wolf Optimizer for Solving the Global Optimization Problem

Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

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