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Flight tracking control of mini-drone: flower pollination algorithm based model-free control

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第6期239卷 578-592页

作者： Glida, Hossam Eddine Abdelghani, Chelihi Chouki, Sentouh Univ Caen Normandie LIS UR 7478 UNICAEN ENSICAEN F-14000 Caen France Univ Biskra Dept Elect Engn Lab LI3CUB Biskra Algeria Constantine 1 Univ Fac Technol Dept Elect Constantine Algeria Univ Polytech Hauts Defrance UMR 8201 CNRS LAMIH Valenciennes France INSA Hauts Defrance Valenciennes France

This paper presents a novel model-free control approach, flower pollination algorithm-based Model-Free Control (FPA-MFC), for trajectory tracking of mini-drone quadrotor unmanned aerial vehicles (UAVs). The proposed approach employs an adaptive estimator based on filtered signals to approximate the nonlinear dynamic functions of the system. This approximator allows the development of a robust decentralized control law able to separately manage the position and attitude dynamics of the drone. The controller design is free of any prior knowledge of the system dynamics, and the control inputs are computed solely from instantaneous input and output measurements. Indeed, this can significantly reduce the computational burden and improve the efficiency of the control algorithm while preserving its simplicity. The design gains of the control law are selected using the metaheuristic flower pollination algorithm to achieve greater trajectory tracking performance and ensure closed-loop system stability. Simulation tests conducted on the Parrot mini drone platform validate the effectiveness and superior performance of FPA-MFC, compared to similar controllers without optimization and using the particle swarm optimization algorithm.

关键词： Mini-drone model-free control optimization algorithms flower pollination algorithm

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A biomimetic feature extraction method for product packaging colour based on flower pollination algorithm

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International Journal of Product Development 2025年第1期29卷 84-99页

作者： Zhang, Ting Liu, Rui-Zhi Li, Wei School of Design and Art Shanghai Dianji University Shanghai China Mechanical College Shanghai Dianji University Shanghai China Shanghai Weiyi Information Technology Co. Ltd. Shanghai China

In order to improve the accuracy and recall of product packaging colour bionic feature extraction, this paper proposes a product packaging colour bionic feature extraction method based on flower pollination algorithm. Firstly, smooth the product packaging image, extract super-pixel blocks from the image, and complete intelligent image segmentation. Then, using the flower pollination algorithm to simulate self-pollination, the conversion probability is introduced to conduct a global search for colour biomimetic features and determine the colour biomimetic features. Finally, using the sigmoid function, the determined colour biomimetic features are discretised and extracted. If the feature value is 0, the feature is not extracted, and if the value is 1, the feature is extracted to complete the colour biomimetic feature extraction. The results show that the extraction accuracy of the method in this paper can reach 99.25%, the extraction recall rate can reach 96.2% and the product packaging colour biomimetic feature effect is good. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： colour biomimetic feature extraction flower pollination algorithm product packaging sigmoid function swarm intelligence optimisation algorithm

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flower pollination algorithm for Power Quality Enhancement in Smart Grids

Flower Pollination Algorithm for Power Quality Enhancement i...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Satyabrata Sahoo Sarat Chandra Swain Subhra Debdas Suchismita Roy Geetanjali Dei Chitralekha Jena School of Electrical Engineering KIIT DU Bhubaneswar Odisha India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

In modern power systems, maintaining power quality has become a critical challenge, especially with the implementation of smart grids. Active Power Filters (APFs) are widely used to address these issues due to their ability to rapidly filter distortions and provide immediate compensation. To manage both voltage supply disturbances and load current variations, the Unified Power Quality Conditioner (UPQC) is commonly employed. The UPQC integrates both shunt and series active power filters in a unified system, utilizing a shared dc link capacitor. Effective regulation of the dc link capacitor voltage is crucial for the optimal performance of the UPQC. A PI controller typically used for this purpose. This study focuses on optimizing the PI controller parameters of the shunt active filter in the UPQC using the flower pollination algorithm (FPA) to enhance power quality. A MATLAB simulation model of a microgrid incorporating the UPQC was developed, and the results have been analyzed and discussed.

关键词： PI control Power quality flower pollination algorithm Active filters Harmonic analysis Mathematical models Smart grids Voltage control Power harmonic filters Load modeling

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Optimizing Distributed Generator Placement in Distribution Networks Using flower pollination algorithm with Self-Healing Integration

Optimizing Distributed Generator Placement in Distribution N...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Kusuma, Vicky Andria Wibowo, Rony Seto Uman Putra, Dimas Fajar Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798331513320

This study aims to optimize power flow in distribution network systems using artificial intelligence, specifically the flower pollination algorithm (FPA), integrated with a self-healing system to improve the reliability and efficiency of power distribution. The research focuses on mitigating common power distribution issues such as under-voltage and over-voltage by strategically placing Distributed Generators (DG) within the network. A modified IEEE 15-bus system is used as a test case to evaluate the proposed method. The FPA algorithm is chosen for its effectiveness in solving multi-objective optimization problems, incorporating both global and local pollination strategies. The objective function considers total power losses and voltage deviations to ensure optimal DG placement. Simulation results demonstrate that the combined application of FPA and self-healing mechanisms significantly reduces power losses and voltage fluctuations, enhancing overall network stability. This research provides a robust and efficient solution for modern power distribution systems, improving their adaptability to dynamic operational conditions. © 2025 IEEE.

关键词： Distributed Generator flower pollination algorithm IEEE 15 Bus System Optimal Power Flow Self-Healing System

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Optimizing Distributed Generator Placement in Distribution Networks Using flower pollination algorithm with Self-Healing Integration

Optimizing Distributed Generator Placement in Distribution N...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Vicky Andria Kusuma Rony Seto Wibowo Dimas Fajar Uman Putra Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

关键词： Voltage fluctuations Power distribution flower pollination algorithm Distribution networks Power system stability Stability analysis Generators Smart grids Optimization Load flow

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flower pollination algorithm with Bee Pollinator for cluster analysis

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INFORMATION PROCESSING LETTERS 2016年第1期116卷 1-14页

作者： Wang, Rui Zhou, Yongquan Qiao, Shilei Huang, Kang Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to trap into the local optimal. For overcoming these disadvantages of the k-means method, flower pollination algorithm with Bee Pollinator is proposed. Discard pollen operator and crossover operator are applied to increase diversity of the population, and local searching ability is enhanced by using elite based mutation operator. Ten data sets are selected to evaluate the performance of proposed algorithm. Compared with DE, CS, ABC, PSO, FPA and k-Means, the experiment results show that flower pollination algorithm with Bee Pollinator has not only higher accuracy but also higher level of stability. And the faster convergence speed can also be validated by statistical results. (C) 2015 Elsevier B.V. All rights reserved.

关键词： flower pollination algorithm Randomized algorithms Discard pollen operator Elite based mutation operator Crossover operator Clustering problem

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flower pollination algorithm: a comprehensive review

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第4期52卷 2533-2557页

作者： Abdel-Basset, Mohamed Shawky, Laila A. Zagazig Univ Fac Comp & Informat Dept Operat Res El Zera Sq Zagazig Sharqiyah Egypt

flower pollination algorithm (FPA) is a computational intelligence metaheuristic that takes its metaphor from flowers proliferation role in plants. This paper provides a comprehensive review of all issues related to FPA: biological inspiration, fundamentals, previous studies and comparisons, implementation, variants, hybrids, and applications. Besides, it makes a comparison between FPA and six different metaheuristics such as genetic algorithm, cuckoo search, grasshopper optimization algorithm, and others on solving a constrained engineering optimization problem. The experimental results are statistically analyzed with non-parametric Friedman test which indicates that FPA is superior more than other competitors in solving the given problem.

关键词： flower pollination algorithm Metaheuristic Cuckoo search Genetic algorithm Optimization

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flower pollination algorithm parameters tuning

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SOFT COMPUTING 2021年第22期25卷 14429-14447页

作者： Mergos, Panagiotis E. Yang, Xin-She City Univ London Dept Civil Engn London EC1V 0HB England Middlesex Univ London Design Engn & Math London England City Univ London Res Ctr Civil Engn Struct Struct Engn London EC1V 0HB England

The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost.

关键词： Optimization Metaheuristics Evolutionary flower pollination algorithm Parameters tuning

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flower pollination algorithm for distribution system phase balancing considering variable demand

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MICROPROCESSORS AND MICROSYSTEMS 2020年 74卷 103008-103008页

作者： Mahendran, G. Govindaraju, C. Builders Engn Coll Dept Elect & Elect Engn Nathakadaiyur 638108 Tamil Nadu India Govt Coll Engn Dept Elect & Elect Engn Salem Tamil Nadu India

Power Distribution systems are one of the most critical links between utility and utility customer. Control of power loss in distribution systems are very much essential considering the economical aspects. Reconfiguration, Capacitor Placement and Phase Balancing are the different methods practiced for power loss reduction. Due to the unbalanced nature of the distribution system, phase balancing is considered as the effective one amongst the above mentioned three methods. Unbalanced feeders not only increase power losses and the risk of overload situations, but they also affect power quality and electricity price. A severely unbalanced circuit can result in excessive voltage drops on the heavily phase. Even a feeder system is designed as a balanced feeder based on the given load data, load prediction errors and unbalanced load growth will induce feeder imbalance. Phase swapping is a direct and effective way to balance a feeder in terms of phases. It has been practiced by engineers based on their previous experiences, and trial and error for phase balancing. In this paper, a phase swapping algorithm based on hybrid Fuzzy-flower pollination algorithm (FFPA) has been developed to minimize the phase current deviation amongst the phases. flower pollination algorithm is used to optimize the fitness value and fuzzy used to format the fitness function integrating multi-objectives. The validation of the proposed algorithm is done through two standard test cases and simulation results are compared with literature. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Distribution system flower pollination algorithm Optimization Heuristic fuzzy Phase balancing Phase swapping

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flower pollination algorithm based solar PV parameter estimation

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ENERGY CONVERSION AND MANAGEMENT 2015年 101卷 410-422页

作者： Alam, D. F. Yousri, D. A. Eteiba, M. B. Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt

Developing a highly accurate simulation technique for Photovoltaic (PV) systems prior to the installation is very important to increase the overall efficiency of using such systems. Providing a more accurate optimization algorithm to extract the optimal parameters of the PV models is therefore continuously required. flower pollination algorithm (FPA) is proposed as a new optimization method to extract the optimal parameters of a single diode and a double diode models. The proposed extraction technique is tested using three different sources of data. The first source is the data reported in the previous literature, while the second source is the experimental data measured at the laboratory. The third source is the experimental data obtained from the data sheets of different types of solar modules. The FPA results are compared with the results of the previous literature to validate the performance of the proposed technique. The results prove that FPA achieves the least error between the extracted and the measured data relative to the other techniques over the entire ranges of different environmental conditions, specially at low irradiation levels. Moreover, FPA outperforms the other techniques from the point of view of both the convergence speed and the convergence time. In addition, comparison of (I-V) characteristics of the extracted parameters by FPA and that of the experimental data shows unnoticed deviation between them. That is why the flower pollination algorithm is recommended as the fastest and the most accurate optimization technique for the optimal parameters extraction process. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： flower pollination algorithm Optimization PV modeling Parameter extraction Solar modules

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