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Transactions of Nanjing University of Aeronautics and Astronautics 2021年第5期38卷 816-828页

作者： ZHOU Ling ZHANG Chunyun BAI Yushuai LIU Kun CUI Miao Key Laboratory of Advanced Technology for Aerospace Vehicles of Liaoning Province State Key Laboratory of Structural Analysis for Industrial EquipmentDalian University of TechnologyDalian 116024P.R.China

Accurately solving transient nonlinear inverse heat conduction problems in complex structures is of great importance to provide key parameters for modeling coupled heat transfer process and the structure’s optimization *** finite element method in ABAQUS is employed to solve the direct transient nonlinear heat conduction *** particle swarm optimization(PSO)method is developed and used to solve the transient nonlinear inverse *** investigate the inverse performances,some numerical tests are *** conditions at inaccessible surfaces of a scramjet combustor with the regenerative cooling system are inversely *** results show that the new methodology can accurately and efficiently determine the boundary conditions in the scramjet combustor with the regenerative cooling *** solving the transient nonlinear inverse problem,the improved particle swarm optimization for solving the transient nonlinear inverse heat conduction problem in a complex structure is verified.

关键词： improved particle swarm optimization transient nonlinear heat conduction problem inverse identification finite element method complex structure

System Identification Method for Small Unmanned Helicopter Based on improved particle swarm optimization

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Journal of Bionic Engineering 2016年第3期13卷 504-514页

作者： Oi Bian Kairui Zhao Xinmin Wang Rong Xie School of Automation Northwestern Polytechnical University Xi 'an 710129 China

This paper proposes a novel method for Small Unmanned Helicopter （SUH） system identification based on improved particle swarm optimization （IPSO）. In the proposed IPSO, every particle will do a local search as a ＂self-check＂ before up- dating the global velocity and position. Then, the global best particle is created by a certain number of elitist particles in order to get a rapid rate of convergence during calculation. Thus both the diversity and convergence speed can be taken into considera- tion during a search. Formulated by the first principles derivation, a state-space model is built for the analysis of dynamic modes of an experimental SUH. The helicopter is equipped with an Attitude Heading Reference System （AHRS） and the corresponding data storage modules, which are used for flight test data measurement and recording. After data collection and reconstruction, the input and output data are utilized to determine the corresponding aerodynamic parameters of the state-space model. The predictive accuracy and fidelity of the identified model are verified by making a time-domain comparison between the responses from the simulation model and the responses from actual flight experiments. The results show that the characteristics of the experimental SUH can be determined accurately using the identified model and the new method can be used for SUH system identification with high efficiency and reliability.

关键词： small unmanned helicopter state-space model system identification improved particle swarm optimization

Research on Reactive Power optimization of Offshore Wind Farms Based on improved particle swarm optimization

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Energy Engineering 2023年第9期120卷 2013-2027页

作者： Zhonghao Qian Hanyi Ma Jun Rao Jun Hu Lichengzi Yu Caoyi Feng Yunxu Qiu Kemo Ding State Grid Jiangsu Nantong Electric Power Co. Ltd.Nantong226000China College of Energy and Electrical Engineering Hohai UniversityNanjing211100China

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind *** improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind ***,the power flow of offshore wind farms is modeled,analyzed and *** improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning *** the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering ***,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of *** the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is ***,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

关键词： Offshore wind farms improved particle swarm optimization reactive power optimization adaptive weight asynchronous learning factor voltage stability

Joint Grid Network and improved particle swarm optimization for Path Planning of Mobile Robot 36

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Joint Grid Network and Improved Particle Swarm Optimization ...

第36届中国控制会议

作者： Xiaoyuan Luo Jiange Wang Xiaolei Li Institute of Electrical Engineering Yanshan University

ISBN: (纸本)9781538629185

This paper deals with the optimal path planning problem for mobile robot which are drove in obstacle ***,we aim at solving the deadlock problem using grid network and improved particle swarm optimization *** proposed method is different from traditional particle swarm optimization method,which generally has high-computational complexity and local ***,we utilize the integrated grid network and improved particle swarm optimization *** the grid network,the long path in the whole workspace with obstacles is divided into multi-segment continuous short *** improved particle swarm optimization is used in the short paths by the *** the grids are prioritized and according to the grids priority in the short path,an integrated algorithm is *** applying the proposed algorithm,the mobile robot move to the target along the optimal path without obstacle collision and ***,some experiments are provided to demonstrate the effectiveness of our proposed algorithm.

关键词： Mobile Robot Path Planning Grid Network improved particle swarm optimization

A Pulse Phase Estimation Method Based on improved particle swarm optimization

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A Pulse Phase Estimation Method Based on Improved Particle S...

第十三届中国卫星导航年会

作者： Yike Qiao Yusong Wang Wei Zheng College of Aerospace Science and Engineering National University of Defense Technology

X-ray pulsar-based navigation（XPNAV） is a celestial autonomous navigation method based on X-ray pulsar observation. The pulse phase estimation has been the key point of XPNAV because the balance of accuracy and efficiency in pulse phase estimation is difficult to obtain. In response to this problem, a pulse phase estimation method based on improved particle swarm optimization is proposed. Experiments were conducted based on simulation data and real data. The results demonstrate that the proposed method significantly reduces the CPU time cost of pulse estimation on the premise of guaranteeing high reliability and accuracy.

关键词： x-ray pulsar-based navigation signal processing improved particle swarm optimization

Optimal Control of Fed-batch Process with improved particle swarm optimization

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Optimal Control of Fed-batch Process with Improved Particle ...

2010 Chinese Control and Decision Conference

作者： Kezhong Lu~1,Haibo Li~2 1.Department of Computer Science,Chizhou College,Chizhou,247100,China 2.Department of Electronic Engineering,Wuxi Vocational Institute of Commercial Technology,Wuxi,214153,China

＜正＞To overcome the problem of premature convergence on particle swarm optimization（PSO）,an improved particle swarm optimization（IPSO） was proposed,which was called keeping particles active *** was guaranteed to keep the diversity of the particle *** particles lose activity,a special mutation or perturbation was used to activate particles and to make particles explore the search space more *** IPSO was used to determine optimal substrate feeding rate for fed-batch alcohol fermentation *** experimental results showed that the IPSO can find better feeding rate quickly than SPSO,and increase alcohol yield more 14 percent than GA.

关键词： improved particle swarm optimization Fed-batch Fermentation Optimal Control Alcohol

On Line Parameter Identification of an Induction Motor Using improved particle swarm optimization

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On Line Parameter Identification of an Induction Motor Using...

第二十六届中国控制会议

作者： Chen Guangyi 1,Guo Wei1,2,Huang Kaisheng2 1.Department of Automation,Foshan University,Foshan 528200,P.R China 2.Faculty of Automation,Guangdong University of Technology,Canton 510090,P.R.China

The paper introduces a improved particle swarm optimization（IPSO）algorithm with dynamic inertia weight and applies this method to parameter identification of induction machine including the effects of *** machine dynam-ics can be presented as a set of time-varying differential equations with machine saturated inductances modeled by nonlinear functions of exciting current [9].Based on the data acquired from the 1.1 kw induction motor,a comparison between the real parameters response with that determined by the proposed algorithm have been presented,and the result of identification us-ing the GA（genetic algorithm）and standard particle swarm optimization algorithm have also been *** results show that the performance of the IPSO is better than other *** is concluded that IPSO is a effective algorithm for pa-rameters identification.

关键词： improved particle swarm optimization Induction motor Parameter identification Saturable model

Identification of the Hammerstein nonlinear system with noisy output measurements

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Control Theory and Technology 2024年第2期22卷 203-212页

作者： Qiming Zha Feng Li Ranran Liu School of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001JiangsuChina School of Automobile and Traffic Engineering Jiangsu University of TechnologyChangzhou213001JiangsuChina

In this research, we present a methodology to identify the Hammerstein nonlinear system with noisy output measurements. The Hammerstein system presented is comprised of neural fuzzy model (NFM) as its static nonlinear block and auto-regressive with extra input (ARX) model as its dynamic linear block, and a two-step procedure is accomplished using signal combination. In the first step, in the case of input–output of Gaussian signals, the correlation function-based least squares (CF-LS) technique is utilized to identify the linear block, solving the problem that the intermediate variable connecting nonlinear and linear blocks cannot be measured. In the second step, to improve the identification accuracy of the nonlinear block parameters, an improved particle swarm optimization technique is developed under input–output of random signals. The validity and accuracy of the presented scheme are verified by a numerical simulation and a practical nonlinear process, and the results illustrate that the proposed methodology can identify well the Hammerstein nonlinear system with noisy output measurements.

关键词： Hammerstein nonlinear system Signal combination Auto-regressive with extra input improved particle swarm optimization

Multi objective optimization method for collision safety of networked vehicles based on improved particle optimization

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Journal of Control and Decision 2023年第1期10卷 134-142页

作者： Zhiyi Huo Weize Liu Qian Wang Hebei Vocational University of Technology and Engineering XingtaiPeople’s Republic of China

The crashworthiness and energy absorption optimization of automobile structure is an importantresearch content of the modern automobile industry. Facing the problem that traditionaloptimization methods are difficult to find the optimal solution for multi-objective parametersof vehicle structure crashworthiness problem, a multi-objective optimization method of vehiclecrash safety based on improved particle swarm optimization is proposed. Through the collectionand optimization of vehicle collision safety parameters, the vehicle structure performance isimproved, and the vehicle regression model is constructed. Using this method, the front-endstructure reinforcement of the vehicle is taken as the design variable. In order to realize themulti-objective optimization design method of vehicle collision safety, the multi-objective optimizationparameters of vehicle frontal collision and offset collision are taken as the objectivefunction. Finally, the simulation results show that the multi-objective optimization method basedon an improved particle swarm optimization algorithm has an obvious effect on the optimizationof vehicle structure crash safety.

关键词： improved particle swarm optimization vehicle collision multi-objective optimization

Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on improved PSO and LSSVM

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Energy Engineering 2021年第3期118卷 679-689页

作者： Haibo Liu Yujie Dong Fuzhong Wang School of Electric Engineering and Automation Henan Polytechnic UniversityJiaozuo454000China College of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction ***,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to ***,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying ***,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP *** results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction *** IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.

关键词： Mining face gas outburst least squares support vector machine improved particle swarm optimization prediction