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Hysteresis modelling and feedforward compensation of piezoelectric nanopositioning stage with a modified Bouc-Wen model

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MICRO & NANO LETTERS 2018年第8期13卷 1170-1174页

作者： Ming, Min Feng, Zhao Ling, Jie Xiao, Xiao-Hui Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Hubei Peoples R China

Piezoelectric actuators (PEAs) are widely applied in various nanopositioning equipment. However, the strong hysteresis nonlinearity compromises the positioning accuracy. In this work, a novel modified Bouc-Wen (MBW) model with a polynomial function of the differential of the input is established for modelling the hysteresis nonlinearity of the PEA-actuated nanopositioning stages. The particle swarm optimisation algorithm is adopted to identify the parameters of the MBW model with a set of input-output experimental data. The obtained model with the corresponding identification parameters matches well the experimental data with 0.31% relative error. A feedforward compensator based on the obtained model is also applied to compensate the hysteresis nonlinearity. Experiments are conducted to validate the effectiveness of this approach, and the results show the great improvement of positioning accuracy of the stage.

关键词： control nonlinearities particle swarm optimisation feedforward position control compensation hysteresis vibration control nanopositioning piezoelectric actuators (MBW) model polynomial function PEA particle swarm optimisation algorithm MBW model input-output corresponding identification parameters experimental data 0 31% relative error feedforward compensator positioning accuracy feedforward compensation piezoelectric nanopositioning stage modified Bouc piezoelectric actuators nanopositioning equipment strong hysteresis nonlinearity

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swarm intelligence-based energy-efficient data delivery in WSAN to virtualise IoT in smart cities

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IET WIRELESS SENSOR SYSTEMS 2018年第6期8卷 256-259页

作者： Manshahia, Mukhdeep Singh Punjabi Univ Dept Math Patiala Punjab India

With the rapid implementation and expansion of the Internet of things (IoT) technologies in smart cities, network congestion control and energy-efficient routing in wireless sensor and actuator networks (WSANs) for virtualising IoT have emerged as a crucial area of research in recent years. This study implements the particle swarm optimisation (PSO) algorithm to realise network congestion control and energy-efficient routing in the transport layer of WSANs. This algorithm is based on the flocking behaviour of birds. The simulation results show that the proposed PSO-based approach provides better performance in terms of network lifetime and packet drop ratio compared with the ant colony optimisation and the artificial bee colony algorithm.

关键词： telecommunication network routing particle swarm optimisation Internet of Things wireless sensor networks swarm intelligence telecommunication congestion control actuators smart cities ant colony optimisation energy conservation telecommunication power management swarm intelligence-based energy-efficient data delivery WSAN smart cities network congestion control energy-efficient routing particle swarm optimisation algorithm PSO-based approach network lifetime packet drop ratio Internet of Things technologies wireless sensor and actuator networks IoT virtualisation ant colony optimisation artificial bee colony algorithm

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Estimation of Bearing Capacity of Piles in Cohesionless Soil Using Optimised Machine Learning Approaches

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2020年第2期38卷 2271-2291页

作者： Kardani, Navid Zhou, Annan Nazem, Majidreza Shen, Shui-Long RMIT Univ Sch Engn Civil & Infrastruct Engn Discipline Melbourne Vic 3001 Australia Shantou Univ Coll Engn Dept Civil & Environm Engn Shantou 515063 Guangdong Peoples R China

Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the beta-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches.

关键词： Optimised machine learning algorithms particle swarm optimisation algorithm Bearing capacity of piles Relative variable importance

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Techno-economic potential of battery energy storage systems in frequency response and balancing mechanism actions

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JOURNAL OF ENGINEERING-JOE 2020年第9期2020卷 774-782页

作者： Kirli, Desen Kiprakis, Aristides Univ Edinburgh Inst Energy Syst Sch Engn Faraday BldgKings BldgColin Maclaurin Rd Edinburgh EH9 3DW Midlothian Scotland

Batteries offer a combination of balancing and regulation services within a smart grid to improve its resilience and flexibility. Maintaining an acceptable state of health and the highest rate of return requires dynamic modelling of the asset and rigorous optimisation. The authors compare the technical cost and economic benefit of battery employment in dynamic frequency and balancing mechanism actions in a smart grid. They use the services procured by National Grid in the UK as a case study but the methodology is globally applicable, including developing grid infrastructures. Their methodology yields the most optimum scenario of service participation, accounting for the dynamic degradation and considering variable pricing of electricity throughout the day. Additionally, it advises the most optimal despatch schedule and price declarations for the battery over the course a day and a year, employing particle swarm optimisation algorithm and historic data. Their results demonstrate that ordinarily frequency response is preferred due to its lower technical toll and payments for availability rather than despatch. However, the proposed despatch schedule including both services provides the highest profit. They anticipate this methodology to become the basis for more sophisticated battery models that integrate the service despatch optimisation, dynamic lifetime degradation and economic analysis.

关键词： optimisation smart power grids profitability battery storage plants scheduling pricing energy storage particle swarm optimisation power generation economics secondary cells grid infrastructures optimum scenario service participation dynamic degradation optimal despatch schedule price declarations particle swarm optimisation algorithm ordinarily frequency response lower technical toll highest profit sophisticated battery models dynamic lifetime degradation economic analysis techno-economic potential battery energy storage systems balancing mechanism actions regulation services smart grid flexibility dynamic modelling rigorous optimisation technical cost economic benefit battery employment dynamic frequency National Grid

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Path planning of autonomous agricultural machineries in complex rural road

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JOURNAL OF ENGINEERING-JOE 2020年第6期2020卷 239-245页

作者： Cui, Jinbo Zhang, Xianfeng Fan, Xinmiao Feng, Wei Li, Ping Wu, Yu Chongqing Acad Agr Sci Chongqing Peoples R China Sichuan Normal Univ Coll Movie & Media Chengdu Peoples R China

This study mainly investigates the path planning and collision avoidance problem of autonomous agricultural machineries in the complex rural road. First, the grid model is used to build the global map of machineries and the particle swarm optimisation algorithm is used to search the global path. A non-linear dynamic inertia weight is introduced to avoid the local lock-up problem of traditional particle swarm optimisation. Then the artificial potential energy method for addressing the local collision problem is used, which is the sum of the attraction of the target point and the repulsion of the obstacles and the road boundary force. To solve the problem of the slip and roll of autonomous machinery caused by complex roads, a machinery dynamic model considering road curvature and topographic inclination is established, and the model predictive control algorithm is used to track the planned path. The simulation results show that proposed method can make the autonomous agricultural machinery driving safely and smoothly.

关键词： predictive control collision avoidance particle swarm optimisation agricultural machinery nonlinear dynamic inertia weight traditional particle swarm optimisation road boundary force autonomous machinery model predictive control algorithm autonomous agricultural machinery path planning collision avoidance problem particle swarm optimisation algorithm machinery dynamic model

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Global MPPT of Solar PV Modules using a Dynamic PSO algorithm under Partial Shading Conditions

Global MPPT of Solar PV Modules using a Dynamic PSO Algorith...

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IEEE Conference on Clean Energy and Technology (CEAT)

作者： Huynh, Duy C. Nguyen, Tuong M. Dunnigan, Matthew W. Mueller, Markus A. Univ Technol Ho Chi Minh City Vietnam Heriot Watt Univ Edinburgh Midlothian Scotland Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781479932375

This paper proposes a novel global maximum power point tracking (MPPT) strategy for solar photovoltaic (PV) modules under partial shading conditions using a dynamic particle swarm optimisation (PSO) algorithm. Solar PV modules have non-linear V-P characteristics with local maximum power points (MPPs) under partial shading conditions. In order to continuously harvest maximum power from solar PV modules, it always has to be operated at its global MPP which is determined using the proposed dynamic PSO algorithm. The obtained simulation results are compared with MPPs achieved using the standard PSO, and Perturbation and Observation (P&O) algorithms to confirm the effectiveness of the proposed algorithm under partial shading conditions.

关键词： Solar photovoltaic modules maximum power point tracking partial shading conditions particle swarm optimisation algorithm

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Research on the Price Prediction Model of the Used New Energy Vehicles Based on PCA-PSO-BP Neural Network 5

Research on the Price Prediction Model of the Used New Energ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Liu, Bowen Wu, Zhongcheng Anhui Univ Inst Phys Sci & Informat Technol Hefei 230031 Peoples R China Chinese Acad Sci High Magnet Field Lab HFIPS Hefei 230031 Peoples R China

ISBN: (纸本)9798350386783;9798350386776

A BP neural network evaluation model based on principal component analysis and particle swarm optimisation algorithm (PCA- PSO-BP) is proposed to address the problems of single selection indicators, excessive reliance on manual detection, and large evaluation error in the existing residual value evaluation of used new energy vehicles. Firstly, the principal component analysis method is used to screen out the most important feature indicators that affect the residual value of new energy vehicles. Then, the particle swarm optimization algorithm is used to optimize the weights and thresholds of the BP neural network. Finally, the selected features are input into the optimized BP neural network to output the residual value evaluation result. The results indicate that the PCA-PSO-BP neural network residual value evaluation model has high prediction accuracy and can provide reference for the current state of the art. The results indicate that the PCA-PSO-BP neural network residual value evaluation model has high prediction accuracy and can provide reference for the current residual value evaluation methods of second-hand new energy vehicles.

关键词： new energy vehicles residual value evaluation particle swarm optimisation algorithm bp neural network

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A PSO-BP Target Localization Accuracy Enhancement Method for Airborne Optoelectronic Platforms 3

A PSO-BP Target Localization Accuracy Enhancement Method for...

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3rd International Conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Chen, Kuiyu Lei, Zhongkui Huang, Daqing Su, Nan Ling, Zhicheng College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Advanced Technology for Medium and Small UAV Nanjing210016 China College of Marxism Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9798350359961

This study aims to address the issue of target positioning accuracy in airborne optoelectronic platforms. Firstly, the paper refines and summarizes the typical workflow for target positioning based on UAV airborne optoelectronic platforms. To enhance the target positioning accuracy of airborne optoelectronic platforms, an improved method based on the particle swarm optimization algorithm (PSO) and the backpropagation neural network (BP) is proposed. The paper begins by explaining the research background and identifying the problems. It then proposes a target localization model based on the optoelectronic platform. Subsequently, the PSO-BP method is introduced, highlighting its advantages, principles, and implementation process. Experimental results demonstrate that the PSO-BP method can effectively enhance the target localization accuracy of airborne optoelectronic platforms. The research findings of this paper hold significant theoretical importance and practical value in improving the target localization accuracy of airborne optoelectronic platforms. Additionally, they offer a new research direction for further advancements in UAV target localization. In conclusion, this comprehensive literature review provides insights into the research gap and research motivation in the field of target positioning accuracy in airborne optoelectronic platforms. The proposed PSO-BP method demonstrates its effectiveness in enhancing target localization accuracy. The findings contribute to the theoretical understanding and practical application of improving target localization in airborne optoelectronic platforms, and they also suggest avenues for future research in UAV target localization. © 2023 IEEE.

关键词： Airborne optoelectronic platform BP neural network particle swarm optimisation algorithm Target positioning accuracy

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Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 190卷 106424-106424页

作者： Zhao, Long Zhao, Xinbo Zhou, Hanmi Wang, Xianlong Xing, Xuguang Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Shaanxi Peoples R China

Accurately estimating reference crop evapotranspiration (ETo) is crucial for water resource management and precise irrigation. The FAO-56 Penman-Monteith model has the highest accuracy in terms of ETo prediction, but its application is restricted by a lack of complete meteorological data. To develop a high accuracy prediction model for ETo with incomplete meteorological data, this study used four principal component analysis methods (i.e. path analysis, stepwise linear regression analysis, logistic regression analysis and factor analysis) to determine few factors that strongly affect ETo as the input to established the ETo model with machine learning (ML). The prediction model for ETo was established using support vector machine (SVM), gradient boosting decision tree (GBDT), particle swarm optimisation (PSO)- SVM, and PSO-GBDT algorithms. This study demonstrated that SVM, GBDT, PSO-SVM and PSO-GBDT models can maintain the highest accuracy by using the five most important affect factors extracted on the basis of path analysis (PA) as input, for various models, the precision ranges of NSE, R2, RMSE, MAE, and GPI were approximately 0.53-0.70, 0.63-0.84, 0.27-0.47, 0.27-0.69, and 0.72-1.01 respectively. The accuracy of SVM and GBDT significantly improved through optimisation using PSO. When inputting the top five factors of importance extracted by PA algorithm in each model, the SVM and GBDT model optimized by PSO algorithm increased the average value of GPI by 17.74% and 46.71% respectively. When inputting the top five factors extracted by the PA algorithm in PSO-GBDT, the average value of NSE, R2, RMSE and MAE is 22.37%, 2.83%, 9.05% and 6.84% higher than that of PSO-SVM, and the GPI is 8.30% higher than that of PSO-SVM. This study shows that the ETo prediction model constructed by the PSO-GBDT algorithm can obtain the highest accuracy when the top five factors extracted by the PA algorithm are used as input. This provides a reference for ETo estimation in Southwest C

关键词： Reference crop evapotranspiration Principal component analysis particle swarm optimisation algorithm Support vector machine Gradient boosting decision tree

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Multi-objective optimal design of passive power filter for aircraft starter/generator system application

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JOURNAL OF ENGINEERING-JOE 2018年第13期2018卷 636-641页

作者： Zhao, Dan Liu, Weiguo Shen, Ke Zhao, Guodong Wang, Xin Northwestern Polytech Univ Sch Automat Inst REPM Elect Machines & Control Technol Xian Shaanxi Peoples R China

A multi-objective optimisation design method for passive power filter devices is proposed to solve the input current harmonic suppression problem of the aircraft starter/generator system. The parameters of the filter device are optimised by using the particle swarm optimisation algorithm. The proposed algorithm takes harmonic parameters, DC voltage drop, total volume and weight as the optimisation target. With these optimisations, the filter has the minimal volume and weight, while the current harmonic and the DC side voltage drop are able to meet the power quality requirements. This approach improves the filter design procedure with advantages of the reduced amount of calculation. Finally, theoretical analysis and computer simulation validate the effectiveness and viability of the presented algorithm.

关键词： starting passive filters power supply quality particle swarm optimisation aircraft power systems power harmonic filters minimal volume DC side voltage drop power quality requirements filter design procedure multiobjective optimal design multiobjective optimisation design method passive power filter devices input current harmonic suppression problem particle swarm optimisation algorithm harmonic parameters DC voltage drop optimisation target aircraft starter-generator system application

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