Robust Control for UAV Close Formation Using LADRC via Sine-Powered Pigeon-Inspired Optimization

Yuan, Guangsong; Duan, Haibin

doi:10.3390/drones7040238

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…Sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for each channel. Simulation results show that the designed control system achieves stable and robust dynamic performance within the expected error range, maximizing the aerodynamic benefits for a trailing UAV [543]. Jing et al have proposed a disturbance-observer-based nonlinear sliding mode surface controller (SMC) for a simulated PX4-conducted quadcopter and optimized its parameters using PSO.…”

Section: Optimal Guidance and Controlmentioning

confidence: 99%

“…[530] 2020 Control parameter tunning CS [531] 2016 Control parameter tunning DE [532] 2016 Control parameter tunning DE [533] 2021 Control parameter tunning FA [534] 2021 Control parameter tunning FA [535] 2015 Control parameter tunning FA [536] 2022 Control parameter tunning FA [537] 2018 Control parameter tunning FAO [538] 2019 Control parameter tunning FPA [539] 2020 Control parameter tunning GSO [540] 2017 Control parameter tunning GSO [541] 2021 Control parameter tunning HS [542] 2020 Control parameter tunning HHO [543] 2023 Control parameter tunning PIO [544] 2022 Control parameter tunning PSO [545] 2022 Swarm motion and formation BeeA [546] 2019 Swarm motion and formation DE [547] 2019 Swarm motion and formation DE [548] 2020 Swarm motion and formation GWO [549] 2022 Swarm motion and formation MFO [550] 2023 Swarm motion and formation PSO [551] 2020 Swarm motion and formation GA [552] 2023 Swarm motion and formation ACO, DE [553] 2017 Swarm motion and formation HS [554] 2022 Swarm mission planning and task allocation FAO 2019 Swarm motion and formation DE [547] 2019 Swarm motion and formation DE [548] 2020 Swarm motion and formation GWO [549] 2022 Swarm motion and formation MFO [550] 2023 Swarm motion and formation PSO [551] 2020 Swarm motion and formation GA [552] 2023 Swarm motion and formation ACO, DE [553] 2017 Swarm motion and formation HS [554] 2022 Swarm mission planning and task allocation FAO [555] 2022 Swarm mission planning and task allocation FAO [556] 2022 Swarm mission planning and task...…”

Section: Reference Publication Year Application Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

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Section: Optimal Guidance and Controlmentioning

confidence: 99%

Section: Reference Publication Year Application Algorithmmentioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…To solve the multi-target optimization problem, a multi-target pigeon swarm optimization algorithm based on the Pareto sorting mechanism and merging operators has emerged. At present, MOPIO has been successfully applied to solving multi-target optimization problems such as multi-UAV target searching and the fuzzy production scheduling problem [31][32][33]. has emerged.…”

Section: Multi-objective Pigeon-inspired Optimization (Mopio) Algorithmmentioning

confidence: 99%

“…has emerged. At present, MOPIO has been successfully applied to solving multi-target optimization problems such as multi-UAV target searching and the fuzzy production scheduling problem [31][32][33].…”

Section: Multi-objective Pigeon-inspired Optimization (Mopio) Algorithmmentioning

confidence: 99%

The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm

Wang,

Ge,

Duan

et al. 2023

Energies

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The output of wind power and photovoltaic power is random, fluctuating and intermittent, and a direct grid connection will result in the reduction of power generation income and a great fluctuation in the power grid’s connection. The addition of hydropower stations can reduce the above problems. Therefore, this paper first introduces and analyzes a typical application scenario of a water–wind–light combined power generation system. Then, a multi-objective optimization model is established, considering the two objectives of maximizing the joint generation and minimizing the system’s power fluctuation. Third, the adaptive fractional order calculus strategy is introduced, and a multi-objective pigeon swarm algorithm, which can adaptively adjust the fractional order according to the location information of a flock, is proposed. Finally, an optimization simulation is carried out. The simulation results show that the improved multi-objective pigeon swarm algorithm has better optimization accuracy. It provides a reference for the future implementation of hydropower stations, and the surrounding wind and photoelectric field joint dispatching strategy.

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“…UAV swarm formation flight involves multiple UAVs flying in a specific formation to achieve cooperative operation, task division, and information sharing. The technology has wide-ranging applications in military, civil, scientific research, and entertainment fields [1][2][3][4][5][6][7][8][9][10]. To control multiple UAVs in a specific formation, it is primum necessary to localize them [7].…”

Section: Introductionmentioning

confidence: 99%

A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning

Kang,

Deng,

Yan

et al. 2024

Drones

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UAV swarm passive positioning technology only requires the reception of electromagnetic signals to achieve the positioning and tracking of radiation sources. It avoids the active positioning strategy that requires active emission of signals and has the advantages of good concealment, long acting distance, and strong anti-interference ability, which has received more and more attention. In this paper, we propose a new UAV swarm formation flight method based on pure azimuth passive positioning. Specifically, we propose a two-circle positioning model, which describes the positional deviation of the receiving UAV using trigonometric functions relative to the target in polar coordinates. Furthermore, we design a two-step adjustment strategy that enables the receiving UAV to reach the target position efficiently. Based on the above design, we constructed an optimized UAV swarm formation scheme. In experiments with UAV numbers of 8 and 20, compared to the representative comparison strategy, the proposed UAV formation scheme reduces the total length of the UAV formation by 34.76% and 55.34%, respectively. It demonstrates the effectiveness of the proposed method in the application of assigning target positions to UAV swarms.

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Robust Control for UAV Close Formation Using LADRC via Sine-Powered Pigeon-Inspired Optimization

Cited by 5 publications

References 24 publications

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

The Multi-Objective Optimal Scheduling of the Water–Wind–Light Complementary System Based on an Improved Pigeon Flock Algorithm

A New Method of UAV Swarm Formation Flight Based on AOA Azimuth-Only Passive Positioning

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