Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field

Li, Kun; Yan, Xinxin; Han, Ying

doi:10.1016/j.asoc.2023.111033

Cited by 25 publications

(4 citation statements)

References 29 publications

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“…The parameter ρ will affect the stability, response speed and anti-interference ability of the MFAC controller. Fuzzy control has certain fault tolerance for system parameter changes and external disturbances, and can maintain the stability of the system to a certain extent [36,37]. It is suitable for parameter setting.…”

Section: 3 On-line Correction Of the Controller Parameter ρ By Fuzzy ...mentioning

confidence: 99%

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

Wang,

Li,

et al. 2024

Phys. Scr.

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Greenhouse agriculture has become an essential part of modern agriculture. The proper temperature is essential for greenhouse crops. Therefore, it is necessary to determine an appropriate greenhouse temperature automatic control strategy for greenhouse environment control. A model-free adaptive greenhouse temperature control strategy is proposed by combining a linear extended state observer and a fuzzy controller. Specifically, the linear extended state observer is introduced to estimate model approximation error generated by pseudo gradient estimation. The fuzzy controller is used to correct the system parameter online. In the absence of disturbances, compared with the typical model-free adaptive scheme, the MAE and RMSE of the proposed scheme are reduced by 57.55 % and 5.40 %, respectively. In the presence of custom disturbances, the proposed control scheme still has a faster response speed, stronger robustness, and higher control accuracy. And the proposed method can reduce the frequent action of the actuator, thus effectively reducing the energy cost.

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Section: 3 On-line Correction Of the Controller Parameter ρ By Fuzzy ...mentioning

confidence: 99%

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

Wang,

Li,

et al. 2024

Phys. Scr.

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“…Multiple unmanned aerial vehicle (UAV) systems have a wide range of applications, including patrol inspection [1], urban rescue [2,3], and military surveillance [4][5][6]. In most scenarios, using global navigation satellite systems (GNSSs) to obtain an accurate position is the key to successfully completing various applications.…”

Section: Introductionmentioning

confidence: 99%

“…1 , w Divide the observation interval [w min , w max ] into K cells with the same width, and count the number of particles {w…”

mentioning

confidence: 99%

Multi-UAV Cooperative Localization Using Adaptive Wasserstein Filter with Distance-Constrained Bare Bones Self-Recovery Particles

Xin,

Pan,

Wang

et al. 2024

Drones

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Aiming at the cooperative localization problem for the dynamic UAV swarm in an anchor-limited environment, an adaptive Wasserstein filter (AWF) with distance-constrained bare bones self-recovery particles (CBBP) is proposed. Firstly, to suppress the cumulative error from the inertial navigation system (INS), a position-prediction strategy based on transition particles is designed instead of using inertial measurements directly, which ensures that the generated prior particles can better cover the ground truth and provide the uncertainties of nonlinear estimation. Then, to effectively quantify the difference between the observed and the prior data, the Wasserstein measure based on slice segmentation is introduced to update the posterior weights of the particles, which makes the proposed algorithm robust against distance-measurement noise variance under the strongly nonlinear model. In addition, to solve the problem of particle impoverishment caused by traditional resampling, a diversity threshold based on Gini purity is designed, and a fast bare bones particle self-recovery algorithm with distance constraint is proposed to guide the outlier particles to the high-likelihood region, which effectively improves the accuracy and stability of the estimation. Finally, the simulation results show that the proposed algorithm is robust against cumulative error in an anchor-limited environment and achieves more competitive accuracy with fewer particles.

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“…Nowadays, swarm intelligence optimization algorithms have been studied more deeply, and more and more emerging algorithms have been proposed: sparrow search algorithm (SSA) (Xue and Shen, 2020), arithmetic optimization algorithm (AOA) (Agushaka and Ezugwu, 2021), slime mold algorithm (SMA) (Li et al, 2020), fruit fly optimization algorithm (FOA) (Pan, 2012) and its combination with honey badger algorithm (Li et al, 2024) etc. Based on the continuous development of swarm intelligence optimization algorithms, research on optimized prediction models has received extensive attention.…”

Section: Introductionmentioning

confidence: 99%

A wind speed interval prediction method for reducing noise uncertainty

Li,

Liu,

Han

2024

Wind Engineering

Self Cite

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Due to the noise uncertainty, the conventional point prediction model is difficult to describe the actual characteristics of wind speed and lacks a description of the wind speed fluctuation range. In this paper, the kernel density estimation according to its error value is given, and then its fluctuation range is found to combine the prediction results of the test set to get its prediction range. Firstly, the singular spectrum analysis (SSA) is introduced to conduct the noise reduction, and variational modal decomposition (VMD) is performed to handle the sequences, then an improved slime mold algorithm (SMA) is proposed to optimize the VMD, and the stochastic configuration networks (SCNs) is applied to perform the prediction. Finally, the interval prediction results are calculated by fusing the point prediction error and kernel density estimation. The experimental results demonstrate that the proposed method can effectively reduce the noise interference in the wind speed prediction.

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Multi-mechanism swarm optimization for multi-UAV task assignment and path planning in transmission line inspection under multi-wind field

Cited by 25 publications

References 29 publications

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

Multi-UAV Cooperative Localization Using Adaptive Wasserstein Filter with Distance-Constrained Bare Bones Self-Recovery Particles

A wind speed interval prediction method for reducing noise uncertainty

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