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

SENSORS 2025年第8期25卷 2462-2462页

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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Forecasting carbon dioxide emissions using adjacent accumulation multivariable grey model

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GONDWANA RESEARCH 2024年 134卷 107-122页

作者： Yang, Wei Qiao, Zhengran Wu, Lifeng Ren, Xiaohang Taghizadeh-Hesary, Farhad Shanxi Univ Inst Management & Decis Taiyuan 030006 Peoples R China Shanxi Univ Sch Econ & Management Taiyuan 030006 Peoples R China Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China Cent South Univ Business Sch Changsha 410083 Peoples R China Tokai Univ TOKAI Res Inst Environm & Sustainabil TRIES Tokyo Japan Lebanese Amer Univ Adnan Kassar Sch Business Beirut Lebanon

Carbon dioxide (CO2) emissions, the primary greenhouse effect catalyst, command global attention due to associated environmental challenges. Urgent carbon reduction is imperative, particularly with scholarly discourse emphasizing the criticality of peak emissions and carbon neutrality. Accurate CO2 emission prediction holds immense importance for shaping effective management policies aimed at emission reduction and environmental mitigation. This study introduces an enhanced multivariable grey prediction model (AGMC(1,N)), utilizing the particle swarm optimization (PSO) algorithm based on artificial intelligence to determine its optimal order. Rigorous analysis, including a disturbance bound classification discussion, validates the superior stability and outstanding predictive capability of the AGMC(1,N) model, as exemplified in a detailed case study. Applying the AGMC(1,N) model to forecast CO2 emissions in the Beijing-Tianjin-Hebei region and Shanxi Province reveals a correlation between energy, primary and secondary industry growth, GDP per capita, and increased emissions, while rising urbanization and natural gas consumption correlate with emission decline. The study concludes with actionable proposals derived from predictive insights, providing valuable support for decision-making by management departments focused on emission reduction. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Emission reduction Grey multivariable prediction model Adjacent accumulation particle swarm optimization algorithm Carbon dioxide prediction

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Research on optimization Method of Discrete Point Cloud Registration Based on Improved particle swarm optimization 23

Research on Optimization Method of Discrete Point Cloud Regi...

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Proceedings of the 7th International Conference on algorithms, Computing and Systems

作者： Rong Guo Yunsheng Mao Zuquan Xiang Xiaosa Zhao School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology China

ISBN: (纸本)9798400709098

The plate forming process based on a three-core rolling bending machine is a crucial technology in industries such as shipbuilding, aerospace, and boiler manufacturing. It enables the formation of single curvature plates like cylinders and cones. However, traditional sheet metal forming inspection methods rely on manual sample testing, resulting in low efficiency, poor precision, and an inability to automate or digitize the forming process. This paper introduces a novel automatic plate curvature detection device that measures discrete point data from sheet metal forming and requires rapid registration of this data for evaluating forming errors. To achieve rapid registration, a particle swarm optimization algorithm based on simulated annealing evolution principles is employed. This algorithm facilitates quick alignment between the discrete point data and theoretical values while analyzing sheet forming errors to guide subsequent automated processing steps. Experimental results demonstrate that the improved particle swarm optimization (PSO) effectively evaluates forming errors, providing valuable guidance for automating steel sheet forming processes.

关键词： curvature detection error evaluation particle swarm optimization algorithm registration calculation

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New weighted coefficients of the average-derivative modeling method based on global optimization algorithms

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ACTA GEOPHYSICA 2024年第2期72卷 619-636页

作者： He, Chen Chen, Jing-Bo Chinese Acad Sci Inst Geol & Geophys Key Lab Petr Resources Res Beijing 100029 Peoples R China Chinese Acad Sci Innovat Acad Earth Sci Beijing 100029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The average-derivative optimal method (ADM) is widely applied in frequency-domain forward modeling for its high accuracy and simplicity. Since tuning weighted coefficients can suppress the numerical dispersion, it is extremely important to adopt a suitable optimization algorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimization algorithms, which are sensitive to the initial value and easy to converge on local optimum. The motivation of this paper is to derive new and more accurate ADM coefficients for 2D frequency-domain elastic-wave equation by the global optimization algorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particle swarm optimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimization algorithm, the global optimization algorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimization algorithms produce more accurate results than the local optimization algorithm.

关键词： Numerical modeling Average-derivative method Global optimization Simulated annealing algorithm particle swarm optimization algorithm

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Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO optimization Parameters

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MATERIALS 2024年第10期17卷 2198页

作者： Liu, Ronggui Zhang, Qing Jiang, Feifei Zhou, Juan He, Jianxia Mao, Zhongyang Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China China State Construction Engn Macau Co Ltd Macau 999078 Peoples R China Nantong Inst Technol Sch Civil Engn Nantong 226000 Peoples R China Nanjing Tech Univ Coll Mat Sci & Engn Nanjing 211800 Peoples R China

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and

关键词： deformation prediction of deep foundation pit particle swarm optimization algorithm variational mode decomposition gated recurrent unit

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Optimal Scheduling of Off-Site Industrial Production in the Context of Distributed Photovoltaics

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ENERGIES 2024年第9期17卷 2156页

作者： Xie, Sizhe Li, Yao Wang, Peng North China Elect Power Univ Dept Elect & Elect Engn Beijing 102206 Peoples R China

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side.

关键词： optimized scheduling distributed photovoltaic LSTM neural network particle swarm optimization algorithm master-slave game electricity market

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Research on Two-stage Isolated AC-DC Converter with PSO Optimized PI Control

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第3期19卷 1619-1631页

作者： Zhou, Kai Li, Zheng Wu, Xiaogang Harbin Univ Sci & Technol Engn Res Ctr Automot Elect Drive Control & Syst In Minist Educ Harbin Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China

AC-DC converter has the advantages of high power density, stable output, easy to control, etc., and is widely used in many industrial fields. In this paper, the two-stage isolated AC-DC converter is the object of study, and the converter consists of a front-stage Boost PFC converter and a rear-pole LLC resonant converter. The small signal perturbation analysis method and the extended function method are used to construct the equivalent mathematical models of the PFC converter and the LLC resonant converter, and analyze their input and output characteristics and frequency response characteristics. The control strategy based on particle swarm algorithm optimized PI is proposed, and the PSO algorithm optimized PI loop control system is designed on this basis. Simulation software is used to compare the performance indexes of PSO optimized PI control method, and an experimental platform is built to verify it. The results show that the isolated converter can not only meet the requirement of sinusoidal input current on the grid side, but also realize the electrical isolation and ensure the output voltage stability. The PSO algorithm is also introduced to automatically adjust the PI parameters according to the operating state of the converter, so that a faster regulation speed can be obtained during the startup of the converter and the sudden change of the load, and the system as a whole has a better dynamic characteristic and steady state characteristic, and the simulation analysis and experimental sessions verify the correctness of the circuit design and the effectiveness of the control strategy.

关键词： AC-DC converter Two-stage isolated Power factor correction particle swarm optimization algorithm

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Enhanced Anti-Lock Braking System Performance: A Comparative Study of Adaptive Terminal Sliding Mode Control Approaches

VEHICLES

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VEHICLES 2025年第1期7卷 14-14页

作者： Khatory, Salma Chafouk, Houcine Mellouli, El Mehdi Natl Sch Appl Sci LISA Lab Fes 30050 Morocco Rouen Normandie Univ ESIGELEC IRSEEM Lab F-76000 Rouen France

Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC faces challenges such as chattering near equilibrium, sensitivity to parameter variations, and delayed convergence. To address these issues, advanced techniques like Terminal Sliding Mode Control (TSMC) and Integral Terminal Sliding Mode Control (ITSMC) have been proposed. TSMC ensures finite-time convergence while mitigating chattering, while ITSMC further handles singularities and disturbances. Additionally, Adaptive Switching Control (ASC) based on particle swarm optimization (PSO) is applied to achieve faster convergence, suppress chattering, and enhance system robustness. The adaptive control law, utilizing a Lyapunov-based approach, is employed to estimate and compensate for external disturbances, further improving system performance under uncertainties. Gain tuning, essential for optimizing system performance and reducing tracking errors, is achieved using the efficient Teaching-Learning-Based optimization (TLBO) algorithm. This study applies TSMC, ITSMC, and ASC-based PSO to an Anti-Lock Braking System (ABS), aiming to enhance robustness, stability, and finite-time convergence while reducing chattering. Stability is analyzed through the Lyapunov theory, ensuring rigorous validation. MATLAB simulations demonstrate the effectiveness of the proposed methods in improving ABS performance, offering a valuable contribution to robust control techniques for systems operating under dynamic and uncertain conditions.

关键词： terminal sliding mode control integral TSMC adaptive control law anti-lock braking system gain-tuning particle swarm optimization algorithm finite-time convergence chattering reduction Lyapunov

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Instrumentation Amplifier Input Impedance Calibration With Machine Learning-Based optimizations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第2期72卷 394-398页

作者： Abdelfattah, Safaa Hussein, Hussein M. E. Shrivastava, Aatmesh Onabajo, Marvin Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, to efficiently control integrated capacitor banks within the IA for the determination of the optimal input impedance. These algorithms offer a significant time reduction compared to a calibration with an exhaustive search, reducing calibration time by a factor of over 10(6) (with four 9-bit digital control words) while conserving computational resources. A prototype platform was developed to automatically optimize a fabricated IA test chip designed with 65-nm CMOS technology, which allows to test the machine learning algorithms using a microcontroller to control the digitally tunable input impedance. With an extra input capacitance of 100 pF, the GA algorithm achieved an input impedance of 1.75 G Omega after four generations (iterations), while the PSO algorithm achieved 1.27 G Omega with five iterations.

关键词： Impedance optimization Genetic algorithms Capacitors Calibration Capacitance Machine learning algorithms Cables particle swarm optimization Impedance measurement Input impedance calibration instrumentation amplifier genetic algorithm particle swarm optimization algorithm negative capacitance generation feedback (NCGFB)

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Fuzzy Adaptive Whale optimization Control algorithm for Trajectory Tracking of a Cable-Driven Parallel Robot

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 5149-5160页

作者： Zhou, Bin Wang, Yuhang Zi, Bin Zhu, Weidong Hefei Univ Technol Sch Mech Engn Hefei 230027 Peoples R China Univ Maryland Baltimore Cty Dept Mech Engn Baltimore MD 21250 USA

This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are established. Thus, new fuzzy rules are developed and a new fuzzy PID controller is designed. Subsequently, the AWOA is introduced to optimize quantization and scale factors of the fuzzy PID controller to obtain the optimal solution. Among them, AWOA is an improvement on WOA. Numerical examples show that the fuzzy PID control strategy based on adaptive whale optimization algorithm (FPID-AWOA) has higher CDPR trajectory tracking accuracy than the traditional fuzzy PID control strategy, the fuzzy PID control strategy based on whale optimization algorithm (FPID-WOA), and the fuzzy PID control strategy based on particle swarm optimization (FPID-PSOA). In comparison with the FPID and FPID-PSOA, the experimental results show that the trajectory tracking error of the proposed FPID-AWOA is reduced by 51.2% and 19.5% in the X-axis direction, respectively, 64.2% and 49.7% in the Y-axis direction, respectively, and 29.1% and 12.2% in the Z-axis direction, respectively.

关键词： Cable-driven parallel robot fuzzy control particle swarm optimization algorithm trajectory tracking whale optimization algorithm

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