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FPGA-Based particle swarm Collaborative Target Localization algorithm for UAV swarms

SENSORS 2025年第8期25卷

作者： Zhang, Chuanhao Li, Changsheng Chen, Zhipeng Li, Haojie Yu, Hang Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

To achieve precise collaborative localization of multiple unmanned aerial vehicles (UAVs) in hardware environments, this paper presents an field-programmable gate array-based particle swarm optimization (PSO) algorithm aimed at enhancing the localization efficiency of multiple nodes targeting a specific object. By leveraging the unique computational capabilities of FPGA, the proposed algorithm integrates optimization strategies, including particle mutation, variable crossover probabilities, and adjustable weights. These strategies collectively enhance the performance of the PSO algorithm in localization tasks. Comparative simulations conducted across a range of operational scenarios demonstrate that the algorithm not only ensures high localization accuracy but also delivers excellent real-time performance and rapid convergence. To further validate the algorithm's practical applicability, a four-node collaborative localization platform was developed, and experiments were carried out. The results confirmed the feasibility of multi-node collaborative localization, underscoring the advantages of the proposed algorithm, such as high accuracy, fast convergence, and robust stability.

关键词： cooperative target localization particle swarm optimization algorithm FPGA deployment and verification algorithm localization capability analysis

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Research on Speed Control of PMSM Based on a New Sliding Mode Reaching Law of Fast Integral Terminal Sliding Mode Control With Iterative-Based High-Gain Disturbance Observer

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第3期61卷 4352-4363页

作者： Shi, Song Wang, Yi Mai, Songping Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China

A novel hybrid control algorithm is proposed in this paper to optimize the performance of the speed control system of permanent magnet synchronous motors (PMSM), allowing it to maintain ideal speed even under external disturbances and torque pulsations. Firstly, a new sliding mode reaching law (NSMRL) is presented and combined with a fast integral terminal sliding mode surface to reduce the system's convergence time without increasing the system's chattering. Secondly, a high-gain disturbance observer based on iterative learning control (ILC-HGO) is combined with the new sliding mode reaching law, enhancing the system's anti-disturbance capability. The stability of the proposed algorithm is theoretically analyzed using the Lyapunov method. Thirdly, MATLAB simulations and hardware-in-the-loop experiments were conducted to demonstrate the proposed algorithm's advantages over traditional approaches, showing faster convergence speed, smaller overshoot, and better robustness. Finally, the particle swarm optimization (PSO) algorithm is introduced to optimize the internal parameters of the control algorithm, further improving the control performance of the algorithm. The main contributions of this paper are as follows: 1) A new sliding mode reaching law (NSMRL) is proposed, significantly reducing the system's convergence time without increasing chattering;2) A high-gain disturbance observer based on iterative learning control (ILC-HGO) is combined with the new sliding mode reaching law, enhancing the system's anti-disturbance capability;3) The proposed algorithm is theoretically and experimentally verified, demonstrating significant improvements in convergence speed, overshoot, and robustness compared to traditional methods;4) The particle swarm optimization (PSO) algorithm is introduced to optimize the control algorithm's internal parameters, further enhancing system performance.

关键词： Convergence Mathematical models Sliding mode control Torque Disturbance observers Robustness Prediction algorithms Permanent magnets particle swarm optimization Velocity control Permanent magnet synchronous motor high-gain disturbance observer iterative learning control a new reaching law particle swarm optimization algorithm

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Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

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Journal of Southeast University(English Edition) 2022年第3期38卷 235-241页

作者： Lei Chunli Zhao Mingqi Liu Kai Song Ruizhe Zhang Huqiang School of Mechanical and Electrical Engineering Lanzhou University of TechnologyLanzhou 730050China

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is ***,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is ***,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN *** experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and *** presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

关键词： temperature prediction high-speed motorized spindle particle swarm optimization algorithm back-propagation neural network robustness

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Mutual inductance identification of non-contact excitation system

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ADVANCES IN MECHANICAL ENGINEERING 2024年第8期16卷

作者： Li, Ke Ding, Jie Sun, Xiaodong Meng, Xuan Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Jiangsu Univ Automot Engn Res Inst Xuefu Rd 31 Zhenjiang 212013 Peoples R China

Due to the presence of brush and slip ring in the excitation method of electrically excited synchronous motors, this article proposes a new excitation method - non-contact excitation system. This method transfers electrical energy from the stator to the rotor through magnetic coupling, replacing slip ring and brush. However, the magnetic coupling coils at the primary and secondary ends of the system will deviate, which will affect motor operation quality. In order to effectively reduce the changes caused by mutual inductance, this article proposes an improved particle swarm optimization algorithm for mutual inductance identification. This improved algorithm can effectively reduce the shortcomings of low accuracy and easy to fall into local optima in particle swarm optimization. Simulation and experimental results show that the improved particle swarm optimization algorithm can improve search accuracy.

关键词： Non-contact excitation wireless power transfer mutual inductance identification particle swarm optimization algorithm

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Application of DSAPSO algorithm in Distribution Network Reconfiguration with Distributed Generation

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Energy Engineering 2024年第1期121卷 187-201页

作者： Caixia Tao Shize Yang Taiguo Li School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more *** complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent ***,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local *** tackle this issue,a more advanced particle swarm optimization algorithm is *** address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning *** Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization *** inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search *** incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the *** analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy *** findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the ***,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply ***,the algorithm exhibits a high level of generality and applicability.

关键词： Reconfiguration of distribution network distributed generation particle swarm optimization algorithm simulated annealing algorithm active network loss

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A Clustering Ensemble Method for Cell Type Detection by Multiobjective particle optimization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第1期20卷 1-14页

作者： Liu, Qiaoming Zhao, Xudong Wang, Guohua Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Northeast Forestry Univ Informat & Comp Engn Coll Harbin 150040 Heilongjiang Peoples R China

Single-cell RNA sequencing (scRNA-seq) is a new technology different from previous sequencing methods that measure the average expression level for each gene across a large population of cells. Thus, new computational methods are required to reveal cell types among cell populations. We present a clustering ensemble algorithm using optimized multiobjective particle (CEMP). It is featured with several mechanisms: 1) A multi-subspace projection method for mapping the original data to low-dimensional subspaces is applied in order to detect complex data structure at both gene level and sample level. 2) The basic partition module in different subspaces is utilized to generate clustering solutions. 3) A transforming representation between clusters and particles is used to bridge the gap between the discrete clustering ensemble optimization problemand the continuous multiobjective optimization algorithm. 4) We propose a clustering ensemble optimization. To guide the multiobjective ensemble optimization process, three cluster metrics are embedded into CEMPas objective functions in which the final clustering will be dynamically evaluated. Experiments on 9 real scRNA-seq datasets indicated that CEMP had superior performance over several other clustering algorithms in clustering accuracy and robustness. The case study conducted on mouse neuronal cells identified main cell types and cell subtypes successfully.

关键词： Clustering ensemble RNA-seq single-cell analysis particle swarm optimization algorithm multi-objective optimization

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APPLICATION OF NONLINEAR BIG DATA ANALYSIS TECHNIQUES IN COMPUTER SOFTWARE RELIABILITY PREDICTION

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第1期25卷 55-64页

作者： Gao, Li Wang, Hai Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Nanning Univ Civil & Architectural Engn Coll Nanning 530000 Peoples R China

This research addresses the difficulties of underfitting, overfitting, and convergence to local minima in artificial neural networks for software dependability prediction. The work specifically focuses on enhancing the performance of the con-ventional PSO-SVM model for software reliability prediction. The analysis of the conventional PSO-SVM model and the special features of software reliability prediction serve as the foundation. An improved PSO-SVM software reliability prediction model is developed and the PSO-SVM model and a Backpropagation (BP) prediction model are compared experimentally. The critical metrics assessed include training error, and efficiency. The experimental results reveal that the training error of the enhanced PSO-LSSVM prediction model diminishes rapidly, levelling off after approximately 200 training generations. The BP prediction model requires 1,733 generations to meet training requirements. Furthermore, the improved PSO-LSSVM prediction model demon-strates significantly higher training efficiency than the BP prediction model. The optimized prediction model exhibits superior adaptability to small sample sizes, swift training, and high prediction accuracy, making it a more suitable choice for software reliability prediction applications.

关键词： particle swarm optimization algorithm Support vector machine Software reliability Prediction Training error Efficiency

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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Cao, Kerang Liu, Chang Yang, Siqi Zhang, Yuxin Li, Lili Jung, Hoekyung Zhang, Shuo Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Shenyang 110142 Peoples R China Shenyang Matern & Child Hlth Hosp Shenyang 110014 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea

Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an XGBoost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-XGBoost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-XGBoost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to XGBoost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.

关键词： Cardiovascular disease Machine learning XGBoost algorithm Multi feature selection particle swarm optimization algorithm Model prediction

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The High-Efficiency optimization Design Method for Two-Stage Miller Compensated Operational Amplifier

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第4期71卷 2029-2033页

作者： Yang, Yunqi Yin, Xiaoyu Chen, Dongdong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

In this brief, a high-efficiency optimization design method is proposed for a two-stage Miller-compensated operational amplifier (TSMCOA). In the proposed method, the parameters and performance metrics of TSMCOA are simulated by Cadence software. Then, the neural network (NN) models are utilized to describe the relationship between its parameters and performance metrics, which can greatly improve simulation efficiency. Based on the performance metrics of TSMCOA, a multi-objective function is established. Then, according to the NN models and multi-objective function, the parameters of TSMCOA are optimized by particle swarm optimization algorithm with linearly decreasing inertia weight (PSO-LDIW). The optimized area is 0.1371 mu m2 72.09 mu 20803

关键词： Multi-objective optimization neural network particle swarm optimization algorithm two-stage Miller-compensated operational amplifier

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PSO-NMPC control strategy based path tracking control of mining LHD (scraper)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-28页

作者： Liu, Ya Peng, Ping-an Wang, Li-guan Wu, Jia-xi Lei, Ming-yu Zhang, Chao-wei Lei, Ru Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Minist Educ Xinjiang Inst Engn Key Lab Xinjiang Coal Resources Green Min Urumqi 830023 Peoples R China

The automation of underground articulated vehicles is a critical step in advancing digital and smart mining. Current nonlinear model predictive control (NMPC) controllers face challenges such as delays in turning on large curvature paths and correction lags during the control of underground the Load-Haul-Dump (LHD). To address these issues, this paper proposes a PSO-NMPC control strategy that integrates a particle swarm optimization algorithm (PSO) into the NMPC controller to enhance path tracking for LHDs. To verify the effectiveness of the proposed PSO-NMPC control strategy, the local path of the tunnels is selected as the simulation path, comparing it with the pure NMPC controller based on the path characteristics of the actual tunnel. The results demonstrate that the improved NMPC controller significantly enhances the trajectory tracking performance of the LHD, with maximum absolute lateral deviations for experimental paths 2, 3, and 5 improved by 89.7%, 72.2%, and 68.9%, respectively. Additionally, the improved NMPC controller exhibits superior performance in paths with large curvature compared to those with very small curvature and straight-line paths, effectively addressing the challenges of turn delay and backward lag in LHD operation, thus providing practical significance.

关键词： Mining LHD Nonlinear model predictive control particle swarm optimization algorithm PSO-NMPC control strategy Path tracking control

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