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Optimal fault-tolerant adaptive fuzzy control of quadrotor UAV: a fixed-time stability approach

INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2025年第5期13卷 1-16页

作者： Bacha, Aymene Chelihi, Abdelghani Glida, Hossam Eddine Sentouh, Chouki Senoussi, Mohamed Achraf Mohamed Khider Univ Dept Elect Engn Lab LI3CUB Biskra Algeria Constantine 1 Univ Fac Technol Dept Elect Constantine Algeria Univ Caen Normandie Lab Syst Engn UR 7478 ENSICAEN UNICAEN Caen France Polytech Univ Hauts De France LAMIH UMR 8201 CNRS Valenciennes France INSA Hauts De France Valenciennes France

This paper introduces an optimal fault-tolerant adaptive fuzzy control approach for quadrotor unmanned aerial vehicles (UAVs). The proposed control framework addresses the challenges of unknown nonlinear dynamics, sensor and actuator faults, as well as external disturbances affecting the drone during flight trajectory tracking. For this purpose, a robust control law was developed by combining the backstepping technique, fixed-time stability approach, and fuzzy logic theory. The objective is to achieve rapid compensation for disturbances and faults to prevent stabilization issues and ensure the quick recovery of the UAV. First, the nonlinear dynamic functions and faults are estimated by an adaptive fuzzy system, which is updated online using output variable measurements to guarantee both robustness and performance of closed-loop system. Then, the tuning parameters are automatically selected using the metaheuristic whale optimization algorithm (WOA) to achieve optimal behavior of the drone. The obtained simulation results demonstrate the effectiveness of the proposed controller and highlight its potential for reliable real-world UAV applications.

关键词： Quadrotor UAV Fuzzy control Fault-tolerant Fixed-time stability whale optimization algorithm

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Optimizing Reliability in Total Artificial Heart Design Through Redundancy Strategies and Swarm-Inspired optimization

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International Journal of Applied and Computational Mathematics 2023年第6期9卷 1-20页

作者： Bhandari, Ashok Singh Rawat, Suhani Sharma, Yashaswi Ram, Mangey Kumar, Akshay Department of Mathematics School of Basic and Applied Sciences Shri Guru Ram Rai University Uttarakhand Dehradun India Department of Mathematics Graphic Era Hill University Uttarakhand Dehradun India Department of Mathematics Computer Science and Engineering Graphic Era (Deemed to be University) Dehardun India Institute of Advanced Manufacturing Technologies Peter the Great St. Petersburg Polytechnic University Saint Petersburg 195251 Russian Federation

With noticeable advancements in medical technology, the seemingly impossible procedures have now come to practical implementation. Artificial Heart is one such device which is developed to replace ineffectual human hearts in order to provide a better quality of life for heart failure patients, making it crucial to hold high reliability. In the proposed work, the TAH is considered for the reliability redundancy allocation problem with an objective of maximizing system reliability. The non-linear mixed integer programming problem is solved by using the recently developed meta-heuristic—whale optimization algorithm. A comparative study is presented for optimized reliability of the system using active and cold standby redundancy strategies. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Active redundancy Artificial heart Cold-standby redundancy Reliability redundancy allocation problem whale optimization algorithm

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Application of whale optimization Technique for Evaluating the Performance of Wind-Driven PMSG Under Harsh Operating Events

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2022年第2期6卷 447-470页

作者： Mahmoud, Mohamed Metwally Ratib, Mohamed Khalid Aly, Mohamed M. Abdel-Rahim, Abdel-Moamen M. Aswan Univ Fac Energy Engn Elect Engn Dept Aswan 81528 Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt

Permanent magnet synchronous generators (PMSGs) have become a promising alternative for wind energy generation systems (WEGSs) because of their optimal power extraction, complete controllability, and improved power quality. The growing penetration of PMSG-based WEGSs into the electrical power system has made its performance analysis an imperative field of research. In this paper, detailed mathematical models for a wind turbine, PMSG, power converters, control system, and grid model are used to study the dynamic behavior and operation of the PMSG-based WEGSs. Optimal torque control is used to operate the wind system at maximum power point tracking (MPPT);in addition, whale optimization algorithm-based PI controllers are utilized for the current control of the machine side converter. Furthermore, a hysteresis controller-based braking chopper system is utilized to improve the fault ride-through (FRT) capability and to keep DC-link voltage within its permissible limits. Two scenarios are studied to evaluate the transient and dynamic response of the system. The first scenario exposes the studied system to a regular grid condition (step-change and random variations in the wind speed profile), while the second scenario exposes it to an irregular grid operation (single-phase and three-phase faults). MATLAB/SIMULINK environment is used to validate the effectiveness and superiority of the proposed control schemes during the studied scenarios. The obtained simulation results assure the viability of the overall proposed system and control schemes in improving the power smoothing capability and dynamic response of the system parameters in addition to operating the wind system at MPPT and realizing the FRT capability. Moreover, keeping a constant DC-link voltage is one of the benefits that would result in increasing the life span of the power converters and reducing the amount of cut-off time of the whole system that may otherwise be caused by their destruction.

关键词： Fault ride through Optimal torque control Permanent magnet synchronous generator Power converter protection whale optimization algorithm Wind energy

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Boosting whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

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Journal of Bionic Engineering 2023年第2期20卷 797-818页

作者： Jie Xing Hanli Zhao Huiling Chen Ruoxi Deng Lei Xiao Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China

whale optimization algorithm(WOA)tends to fall into the local optimum and fails to converge quickly in solving complex *** address the shortcomings,an improved WOA(QGBWOA)is proposed in this ***,quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal ***,a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in *** verify the advantages of QGBWOA,comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10,30,50,and 100 and on CEC 2020 test with dimension ***,the performance results were tested using Wilcoxon signed-rank(WS),Friedman test,and post hoc statistical tests for statistical *** accuracy and speed are remarkably improved,as shown by experimental ***,feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world *** proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.

关键词： whale optimization algorithm Quasi-opposition-based learning Gaussian barebone Image segmentation Feature selection Bionic algorithm

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Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Bai, Tiantian Xu, Mengru Zhang, Taotao Jia, Xianjie Wang, Fuzhi Jiang, Xiuling Wei, Xing Bengbu Med Univ Sch Nursing Bengbu 233000 Anhui Peoples R China Bengbu Med Univ Sch Publ Hlth Bengbu 233000 Anhui Peoples R China Bengbu Med Univ Sch Mental Hlth Bengbu 233000 Anhui Peoples R China Bengbu Med Univ Dept Physiol Bengbu 233000 Anhui Peoples R China

Due to the aging of the global population and lifestyle changes, cardiovascular disease has become the leading cause of death worldwide, causing serious public health problems and economic pressures. Early and accurate prediction of cardiovascular disease is crucial to reducing morbidity and mortality, but traditional prediction methods often lack robustness. This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. Subsequently, the selected feature subsets were integrated into ten classification models, and a comprehensive weighted evaluation was performed based on the accuracy, precision, recall, F1 score, and AUC value of the model to determine the optimal model configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25;while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. The results of this study show that swarm intelligence algorithms can effectively screen key and informative feature sets, significantly improve model classification accuracy, and provide strong support for the early diagnosis o

关键词： Cardiovascular disease Cuckoo search algorithm whale optimization algorithm Machine learning

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AI-driven aquaculture: A review of technological innovations and their sustainable impacts

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Artificial Intelligence in Agriculture 2025年第3期15卷 508-525页

作者： Yang, Hang Feng, Qi Xia, Shibin Wu, Zhenbin Zhang, Yi Institute of Hydrobiology Chinese Academy of Sciences Wuhan430072 China Hubei Key Laboratory of Mineral Resources Processing and Environment Wuhan University of Technology Wuhan430070 China University of Chinese Academy of Sciences Beijing100049 China

The integration of artificial intelligence (AI) in aquaculture has been identified as a transformative force, enhancing various operational aspects from water quality management to genetic optimization. This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector, underscoring the significant enhancements in production efficiency and environmental sustainability. Key AI-driven improvements, such as predictive analytics for disease management and optimized feeding protocols, are highlighted, demonstrating their contributions to reducing waste and improving biomass outputs. However, challenges remain in terms of data quality, system integration, and the socio-economic impacts of technological adoption across diverse aquacultural environments. This review also addresses the gaps in current research, particularly the lack of robust, scalable AI models and frameworks that can be universally applied. Future directions are discussed, emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture. This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture. © 2024 The Authors

关键词： Aquaculture AI integration Predictive analytics Sustainable aquaculture AI disease management AI feeding optimization AHPND Acute Hepatopancreatic Necrosis Disease AI Artificial Intelligence AlphaFamImpute Alpha Family Impute AMA-Net Appearance-Motion Architecture Network ANN Artificial Neural Network ASST Acoustic Spectrogram Signal Transformer AUC Area Under the Curve BFT Biofloc Technology BP Back Propagation BPNN Back Propagation Neural Network CBAM Convolutional Block Attention Module CNN Convolutional Neural Network CVS Computer Vision System DM-Count Density Map Count DCNN Deep Convolutional Neural Network DL Deep Learning DO Dissolved Oxygen EEMD Ensemble Empirical Mode Decomposition EER Equal Error Rate FLOPs Floating Point Operations Per Second FPS Frames Per Second FFINet Feature Fusion Interactive Network FT-IR Fourier Transform Infrared Spectroscopy GA Genetic algorithm GBLUP Genomic Best Linear Unbiased Prediction GELU Gaussian Error Linear Unit GIS Geographic Information System GRU Gated Recurrent Unit GWAS Genome-Wide Association Studies HPO Hyperparameter optimization HSI Hyperspectral Imaging IoT Internet of Things KHV Koi Herpes Virus LCA Linear Calibration algorithm LCNN Local Convolutional Neural Network LDA Linear Discriminant Analysis LGBM Light Gradient Boosting Machine LIA Linear Integration algorithm LSTM Long Short-Term Memory MAE Mean Absolute Error mAP Mean Average Precision MCC Matthews Correlation Coefficient ML Machine Learning ML-KAML Machine Learning - Kernel Additive Model Learning MLP Multilayer Perceptron MSE Mean Squared Error NB Naïve Bayes NIR Near-Infrared NMR Nuclear Magnetic Resonance NSE Nash-Sutcliffe Efficiency PBLUP Pedigree-Based Best Linear Unbiased Prediction PCA Principal Component Analysis PCC Pearson Correlation Coefficient PNN Probabilistic Neural Network PSO Particle Swarm optimization QSAR Quantitative Structure-Activity Relationship QUEST Quick Unbiased Efficient Statistical Tree RAS Recirculating Aquaculture System RF Random Forest RFerns Random Ferns RMSE

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Feature Selection and Parameter optimization of Optimized Extreme Learning Machine for Motor Fault Detection 17

Feature Selection and Parameter Optimization of Optimized Ex...

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IEEE 17th International Conference on the Experience of Designing and Application of CAD Systems (CADSM)

作者： Wu, Dade Hubei Mech & Elect Res & Design Inst Co Ltd Wuhan Peoples R China

ISBN: (纸本)9798350310856

In view of the disadvantage of existing motor fault detection algorithms that they cannot adapt to complex and nonlinear fault categories and the accuracy is relatively low, we proposed one method for feature selection and parameter optimization based on simulated annealing-based whale optimization algorithm (SAWOA) optimized extreme learning machine (ELM) are proposed. Based on the ELM classification model, SAWOA is used to optimize the selection of network input variables and parameters of hidden layer nodes of ELM. To verify the effectiveness of the proposed method, it is applied to the real motor fault detection example, and compared with the existing methods under the same conditions. The experiment results show that the method achieves good performance in aspect of the classification accuracy and reliability, and show its effectiveness and application potential.

关键词： Motor Fault Detection whale optimization algorithm Simulated annealing Feature Selection

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Enhanced path planning algorithm via hybrid WOA-PSO for differential wheeled mobile robots

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Najm, Huda Talib Ahmad, Nur Syazreen Al-Araji, Ahmed Sabah Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia Univ Technol Baghdad Biomed Engn Dept Baghdad Iraq Univ Technol Baghdad Comp Engn Dept Baghdad Iraq

This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the whale optimization algorithm (WOA) and Particle Swarm optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.

关键词： Differential wheeled mobile robot metaheuristic particle swarm optimization path planning whale optimization algorithm

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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine

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SCIENTIFIC REPORTS 2024年第1期14卷 1-27页

作者： Syama, S. Ramprabhakar, J. Anand, R. Meena, V. P. Guerrero, Josep M. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Elect Engn Bengaluru India Natl Inst Technol Jamshedpur Dept Elect Engn Jharkhand 831014 India Tech Univ Catalonia Ctr Res Microgrids UPC CROM Dept Elect Engn Barcelona 08019 Spain ICREA Catalan Inst Res & Adv Studies Pg Lluis Companys 23 Barcelona 08010 Spain Aalborg Univ Ctr Res Micro CROM AAU Energy DK-9220 Aalborg Denmark

With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system. Here, we propose a novel hybrid methodology for wind speed and solar irradiance forecasting. The proposed integrated model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose time series data into a sequence of intrinsic mode functions of lower complexity. Further, permutation entropy is employed to extract the complexity of IMFs for filtering and reconstruction of decomposed components to alleviate the difficulty of direct modeling. Then, a unique swarm intelligence technique, the non-linear dimension learning Hunting whale optimization algorithm (NDLHWOA), is devised to optimize regularized extreme learning machine model parameters to capture the implicit information of each reconstructed sub-series. By integrating a non-linear convergence parameter and the dimension learning hunting approach, the performance of WOA can be drastically enhanced, leading to premature convergence, enhanced population variety, and effective global search. The final prediction outcome is obtained by summing the individual reconstructed sub-series prediction outcomes. To evaluate its efficacy, the proposed model is compared to five well-established models. The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and s

关键词： Regularized Extreme Learning Machines (RELM) Permutation Entropy (PE) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Dimension Learning Hunting (DLH) whale optimization algorithm Solar Irradiance forecasting Wind Speed forecasting

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A Hybrid Data-Driven Deep Learning Prediction Framework for Lake Water Level Based on Fusion of Meteorological and Hydrological Multi-source Data

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NATURAL RESOURCES RESEARCH 2024年第1期33卷 163-190页

作者： Yao, Zhiyuan Wang, Zhaocai Wu, Tunhua Lu, Wen Shanghai Ocean Univ Coll Informat Shanghai 201306 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat River Basin Water Beijing 100038 Peoples R China

Accurate prediction of lake water level is of great significance for flood prevention, reservoir scheduling, and ecological protection. However, the change in lake water level is influenced by multiple factors, and water level data as a time series also have the characteristics of complexity, which leads to difficulty in water level prediction. In view of this, a hybrid CEEMDAN-BiGRU-SVR-MWOA (CBSM) framework is proposed here for lake water level prediction based on multiple sources of environmental, hydrological and meteorological factors. Firstly, the lake water level is decomposed into modal data with different frequencies using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, the modal data are divided into internally and externally controlled items using the maximum information coefficient (MIC). Then, multivariate prediction is performed by combining the external data using bi-directional gated recurrent unit (BiGRU). The prediction results are combined using support vector regression (SVR) for prediction with higher accuracy. A modified whale optimization algorithm (MWOA) is used to optimize the parameters of CEEMDAN and the hyperparameters of BiGRU based on permutation entropy and mean square error, respectively. The proposed CBSM was applied to Dongting Lake, China, and the Nash-Sutcliffe efficiency of the prediction results reached 0.997, which was better than those of other benchmark models or frameworks, and the Diebold Mariano (DM) test further demonstrated the superiority of the proposed CBSM. Thus, this research provides a new and effective method for accurately simulating and predicting lake water levels.

关键词： Water level prediction Complete ensemble empirical mode decomposition with adaptive noise Multi-source fusion Maximum information coefficient Bi-directional gated recurrent unit whale optimization algorithm

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