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International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Liu Mengnan Zhang Yiwen Jin Ying Huan Yingxing Wang Jing Avic Chengdu Aircraft Ind Grp Co Ltd Chengdu Sichuan Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

Aiming at the nonlinear optimization problems in the process of locating radiation source by using moving multiple UAVs in passive detection system. This paper presents a joint location technology of TDOA/FDOA based on particle swarm optimization algorithm to achieve high-precision positioning of the target. The linearly decreasing inertia weight and natural selection mechanism are combined with this new method, which will effectively avoid positioning results falling into local optimal values, increase population diversity and improve the quality of the solution. This method can approximate the global optimal solution without initial target position estimation. In order to be applied to practical engineering applications, the WGS-84 geodetic coordinate system will be introduced to describe the earth model. From the simulation results, the proposed method has higher locating accuracy compared to standard particle swarm optimization algorithm. In the end of this paper, the GDOP positioning accuracy of the target reconnaissance area is analyzed by using different UAV formation layout, which provides some reference value for practical engineering application.

关键词： Passive location Time difference of arrival Frequency difference of arrival particle swarm optimization algorithm Unmanned aerial vehicle

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Variable transmission ratio design of a steer-by-wire system for intelligent vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第16期236卷 9341-9353页

作者： Liu, Zhenyu Xu, Xing Xie, Ju Wang, Feng Su, Pengwei Jiangsu Univ Automot Engn Res Inst 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China

Intelligent vehicles require the ability to switch freely between manual and automatic driving modes, which opens up more possibilities for steer-by-wire. Among them, the design of transmission ratio for both driving modes can improve the steering performance of vehicle. In the manual driving mode, this paper optimizes the yaw rate gain upon the particle swarm optimization (PSO) algorithm and designs a variable steering transmission ratio, which can reduce the driver's burden and improve the vehicle's handling stability. Further, the steering wheel angular rate information and fuzzy control are used to design the high-speed transmission ratio to ensure the safety of the vehicle in high-speed emergency situations. In the automatic driving mode, the transmission ratio values at various vehicle speeds are optimized offline based on the PSO algorithm, and a variable transmission ratio curve is designed to enhance the trajectory tracking ability and the vehicle stability. Finally, the hardware-in-the-loop test results show that the variable transmission ratio designed in the manual driving mode reduces the burden of the driver at low speed and improves the vehicle stability at high speed, especially in emergency situations. It also proves that the optimized design of variable ratio in the automatic driving mode promotes the path-following ability and the vehicle stability.

关键词： Steer-by-wire variable transmission ratio automatic driving particle swarm optimization algorithm hardware-in-the-loop test

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Research on the maximum power point tracking method of photovoltaic based on Newton interpolation-assisted particle swarm algorithm

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Clean Energy 2022年第3期6卷 496-502页

作者： LiMing Wei KaiKai Li School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchunJilin130118China

Solar energy has attracted a lot of attention because it is clean and has no ***,due to the partially shaded condition,the photovoltaic array cannot work uniformly at the maximum power point,resulting in a large power *** improve this problem,the research of the maximum power point tracking(MPPT)algorithm is discussed by *** this paper,an improved particle swarm optimization(PSO)algorithm is proposed to achieve the goal of MPPT,which uses Newton interpolation-assisted conventional *** tracking to the maximum power point,the Newton interpolation method is used to calculate the maximum power point,reduce the number of iterations of the conventional PSO algorithm and reduce the steady-state *** simulation is carried out in MATLAB^(■)/Simulink^(■)and compared with conventional *** results show that the improved PSO has better tracking ac-curacy and speed than the conventional algorithm,and the initial tracking speed is increased by>30%.

关键词： photovoltaic array maximum power point tracking particle swarm optimization algorithm Newton interpolation method

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Fault Diagnosis of Chemical Processes Based on PSO-GCN

Fault Diagnosis of Chemical Processes Based on PSO-GCN

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2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks, AIDLNN 2024

作者： Luo, Chen Yang, Jin School of Aeronautical Engineering Civil Aviation University of China China School of Transportation Science and Engineering Civil Aviation University of China China

ISBN: (纸本)9798331520816

As industrial production advances, ensuring process safety and minimizing failure-related losses is crucial. Detecting abnormal conditions efficiently is key. To address the complexity, high dimensionality, nonlinearity, and time-varying nature of chemical process data, this paper proposes a fault diagnosis method based on particle swarm optimization (PSO) and Graph Convolutional Networks (GCN). The method denoises the data, constructs a graph using a sliding window, and uses PSO to optimize the diagnostic model's parameters and extract features. These features are then classified via a fully connected layer and classifier. Experimental results show that the method outperforms other machine learning and deep learning approaches in accuracy and precision, and excels at detecting minor faults in chemical processes. © 2024 IEEE.

关键词： Chemical Processes Fault Diagnosis Graph Convolutional Networks particle swarm optimization algorithm

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Research on Image Segmentation optimization algorithm based on Chaotic particle swarm optimization and Fuzzy Clustering 2018

Research on Image Segmentation Optimization Algorithm based ...

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7th International Conference on Software and Computer Applications (ICSCA)

作者： Tan Linglong Chen Yehui Li Changkai Anhui Xinhua Univ Elect Commun Engn Coll Hefei Anhui Peoples R China

ISBN: (纸本)9781450354141

Fuzzy C-Means clustering, FCM, is an unsupervised learning algorithm. The algorithm is easily affected by noise points and depends on the initial values. When the sample value is large, the algorithm is easy to fall into local extremum. In this study, the traditional fuzzy clustering algorithm is improved, and the particle swarm optimization algorithm with global optimization ability is applied to the FCM algorithm, and chaotic technology is added. Chaotic variables produce a chaotic sequence based on the current global optimal position, using chaotic sequence has the best fitness value of particles randomly instead of a particle of the particle swarm, the improved algorithm can effectively avoid the stagnation of particles in the iteration, fast search to the global optimal solution, avoid convergence to local extremum. Experimental results indicate that this algorithm overcomes the dependence on the initial clustering centre of FCM, which brings high robustness and segmentation accuracy, and has more faster convergence speed.

关键词： FCM particle swarm optimization algorithm chaotic particle swarm optimization algorithm image segmentation

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Fuzzy-PID Strategy Based on PSO optimization for pH Control in Water and Fertilizer Integration

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IEEE ACCESS 2022年 10卷 4471-4482页

作者： Fu, Changxin Ma, Xiao Zhang, Lixin Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Peoples R China XPCC Ind Technol Res Inst Shihezi 832003 Peoples R China

In the process of crop growth, irrational fertilizer application methods have caused waste of fertilizer resources and water, as well as damaged the soil's structure. To puzzle the above problems out, the research constructs the model of water-fertilizer machine by gathering relevant parameters in the field. Considering the system's plenty of defects and combining it with the MATLAB/simulink system, such as non-linearity, time-varying, large inertia, uncertain mathematical model and severe lag, a fuzzy proportional integral differential control based on particle swarm optimization is proposed in this paper for water and fertilizer integration system. Primarily, the research is done for precision fertilization control of fertilizer integration system and water, and the parameters of fuzzy proportional integral differential gain that schedules controllers are optimized through a particle swarm algorithm. The effectiveness of the suggested controller has been validated by comparing with the control algorithms (proportional integral differential control, fuzzy proportional integral differential control) commonly applied in current fertilizer application systems. Simulation experiments for this research are devised through MATLAB/Simulink simulation platform. Significant improvement in the system's tuning capabilities by incorporating particle swarm algorithm in the hysteretic non-linear system. Eventually, four control algorithms are experimentally validated in this research at different pH values through Experiments care designed in the experimental field. The outcomes demonstrate that the control algorithm in this paper possesses better regulation effect, smaller overshoot, excellent stability and more inadequate rising steady state time compared with the previous controls, which can enables precise control of the fertiliser system.

关键词： Fertilizers Mathematical models Soil Irrigation Crops PI control PD control Integration of water and fertilizer hysteretic system pH value fuzzy PID control particle swarm optimization algorithm

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Iterative fault estimation and fault-tolerant control for a class of nonlinear variant time-delay systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第15期32卷 8486-8506页

作者： Feng Li Du Kenan Xu, Shuiqing Yi, Chai Zhang, Ke Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing Peoples R China Hefei Univ Technol Coll Elect & Automat Engn Hefei Peoples R China Chongqing Univ Coll Automat Chongqing Peoples R China

Fault estimation (FE) and fault-tolerant control (FTC) are remarkable techniques, have achieved great success in many applications such as robot, spacecraft, and industrial assembly line. This article aims to design an iterative-learning scheme based FE and FTC method for a class of nonlinear system with iteration-variant state delay and additive measurement noise. A Luenberger observer in iterative version is proposed to achieve the reconstruction of system state information, which consider the historical observation error in order to improve the observation performance in current iteration. To deal with bounded iteration-variant state delay, an iterative-learning scheme based fault estimator is designed and the convergence is proved. Compared with relevant methods which use system output observation residual to revise the FE result of last iteration, the proposed approach uses filtered system output observation residual in order to reduce the effect of measurement noise. Based on the FE result, FTC using signal compensation technique is employed. In addition, an improved particle swarm optimization algorithm is employed for parameters adaptive tuning. Compared with traditional manual adjustment of parameters, the proposed method can find the optimal parameters and save time of parameter tuning. Finally, three examples are provided to verify the effectiveness of the proposed approach.

关键词： fault estimation fault-tolerant control iteration-variant time-delay iterative-learning scheme measurement noise particle swarm optimization algorithm

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Capacity configuration optimization of a hybrid renewable energy system with hydrogen storage

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2022年第14期19卷 1583-1599页

作者： Li, Guoliang Yuan, Benfeng Ge, Min Xiao, Guoping Li, Tao Wang, Jian-Qiang Chinese Acad Sci Dalian Natl Lab Clean Energy Dalian Peoples R China Univ Chinese Acad Sci Dalian Natl Lab Clean Energy Beijing Peoples R China East China Univ Sci & Technol Engn Res Ctr Large Scale Reactor Engn & Technol State Key Lab Chem Engn Minist Educ Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Appl Phys Key Lab Interfacial Phys & Technol Shanghai 201800 Peoples R China

Different from low-temperature electrolysis systems, the large power consumption for the balance of plant (BOP) of the reversible solid oxide cell (RSOC) system for a high-temperature operating condition needs to be considered in the determination of the capacity configuration of the hybrid renewable energy (HRE) system. To address this issue, a power-based model of the RSOC system is developed and the corresponding capacity configuration strategy especially for the HRE system is also proposed in this paper. An optimization program of a hybrid energy system model composed of the wind turbines (WT), photovoltaic panels (PV), reversible solid oxide cell (RSOC) system, hydrogen storage tank (HST), and battery are presented to urge the minimization of the total system cost, power redundancy, and power shortage. particle swarm optimization (PSO) algorithm is utilized to determine the optimum size and operational energy management within the system. The results show that after the regulation of the energy storage system, the power redundancy and shortage of the hybrid energy system are greatly reduced with the decreases by 85.08% and 64.42%, respectively. Moreover, the power mismatch is still significantly affected by seasons. It is also found that the energy storage efficiency of the RSOC system is within 20% due to the fact that part of the electric energy is consumed by the BOP to maintain the operation of the RSOC system. Thus, more improvements should focus on simplifying the BOP system of the RSOC system and reducing the excess power consumption in the future.

关键词： Renewable energy capacity allocation optimization Reversible Solid Oxide Cell energy storage system particle swarm optimization algorithm

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An Iterative Learning Control Research Based on RBF Neural Network and PSO algorithm 12

An Iterative Learning Control Research Based on RBF Neural N...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Wang, Shouqin Gong, Yan He, Xingshi Xian Polytech Univ Sch Sci Xian 710048 Peoples R China

ISBN: (纸本)9798350321050

In order to solve the problem of trajectory shift, a PSO-DRBFNNILC strategy is designed. The first RBFNN is introduced to estimate the output of the ILC system;second RBFNN is built to adaptively adjust the learning gain matrix in the input update law. PSO algorithm is used to find the optimal search step for the update of the weight, center and radius of the activation function. Convergence analysis shows that the estimation error of the weight of the network and the tracking error of the ILC system are bounded. The effectiveness of the control strategy is verified by numerical simulation.

关键词： Iterative learning control RBFNN particle swarm optimization algorithm

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Research on Bearing Fault Recognition Based on PSO-MCKD and 1D-CNN

Research on Bearing Fault Recognition Based on PSO-MCKD and ...

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Asia-Pacific International Symposium on Aerospace Technology (APISAT)

作者： Wang, Yinling Yin, Xianming Southwest Univ Sci & Technol Engn Technol Ctr Mianyang Sichuan Peoples R China

ISBN: (纸本)9789811926891;9789811926884

This paper proposes a bearing fault feature extraction and recognition method based on particle swarm optimization optimization Maximum Correlated Kurtosis Deconvolution (MCKD) and one-dimensional convolutional neural network to solve the non-stationarity of rolling bearing fault signals, Non-linear and complex characteristics, as well as the problems of noise interference and unclear fault characteristics in the process of fault identification. First, the multi-channel signals of the rolling bearing is analyzed, in order to select the signal containing the impact component as the fault feature. Next, the signal containing the fault feature is filtered through MCKD, where the best parameters of MCKD are obtained by improving the particle swarm algorithm to achieve the feature enhancement of the main signal. Finally, a one-dimensional convolutional neural network (One Dimensional Convolutional Neural Network, 1D-CNN) is used to model the characteristic signals under different damage conditions in order to obtain the fault recognition model of the rolling bearing. The experimental results show that the method can effectively extract the main characteristic signals of the faulty bearing, and realize the accurate identification of the bearing fault in the noisy environment.

关键词： Rolling bearings particle swarm optimization algorithm Maximum correlation kurtosis deconvolution One-dimensional convolutional neural network

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