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Micro-amplitude vibration suppression of a bistable nonlinear energy sink constructed by a buckling beam

NONLINEAR DYNAMICS 2022年第4期108卷 3185-3207页

作者： Zeng, You-cheng Ding, Hu Du, Rong-Hua Chen, Li-Qun Shanghai Univ Sch Mech & Engn Sci Shanghai Inst Appl Math & Mech Shanghai Key Lab Mech Energy Engn Shanghai 200444 Peoples R China Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha 410114 Peoples R China

Effective reduction of micro-amplitude vibration has always been a serious challenge. The nonlinear energy sink (NES) has been proven to be able to reduce vibration at a wide frequency. However, the energy threshold of the NES prevents it from suppressing micro-vibrations. In this paper, a bistable nonlinear energy sink (BNES) based on a buckling beam oscillator is constructed. The threshold of the NES is lowered by the nonlinear dynamic behavior of jumping between wells of the bistable oscillator. The motion equations of the discrete-continuous system are derived by using Hamilton's principle. The approximate analytical solution is obtained and verified numerically. The results show that even when the primary system has a micro-amplitude resonance, the nonlinear cross-well vibration of the BNES can still reduce the vibration. The robustness of the BNES is stronger than that of the tuned mass damper (TMD). The optimal parameters of the BNES are obtained with particle swarm optimization (PSO) algorithm. The result of parameter optimization shows that the energy threshold of the nonlinear energy sink can be effectively lowered. In short, the method based on nonlinear dynamics in this paper provides an effective reduction strategy for micro-vibration in engineering. [GRAPHICS]

关键词： Buckling beam Bistable Micro-amplitude vibration particle swarm optimization algorithm Harmonic balance method

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Real-time implementation and evaluation of gorilla troops optimization-based control strategy for autonomous microgrids

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IET RENEWABLE POWER GENERATION 2022年第14期16卷 3071-3091页

作者： Maged, Nourhan A. Hasanien, Hany M. Ebrahim, Essamudin A. Tostado-Veliz, Marcos Jurado, Francisco Elect Res Inst Power Elect & Energy Convers Dept Cairo Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo 11517 Egypt Univ Jaen Super Polytech Sch Linares Dept Elect Engn Linares Spain

The present work develops a novel gorilla troops optimization algorithm (GTO) for optimal control of an autonomous microgrid. The proposed proportional-integral controller parameters are properly designed by the GTO algorithm. It is based on the integral squared error to minimize the system objective function. The suggested islanded microgrid system operates with several decentralized, electronically interfaced energy resources and their local loads. The new optimal controller is applied to regulate its voltage at the different points of common coupling and its frequency despite the load variation. The performance of the new proposal is compared with the PI controller that was designed by a particle swarm optimization algorithm. It is observed that the microgrid functioning operation is improved even more by applying a GTO-based than the other. The control strategy is thoroughly tested using a Matlab/Simulink simpower-system environment under various microgrid operating situations. It is also implemented with OPAL 4510 real-time hardware in the loop and a rapid control prototyping module. The different benchmark experiments serve to show the superiority of the new optimizer.

关键词： PI control power engineering computing power generation control distributed power generation islanded microgrid system real-time implementation PI controller optimal controller microgrid functioning operation GTO algorithm power system simulation optimal control particle swarm optimisation proportional-integral controller parameters rapid control prototyping module autonomous microgrid particle swarm optimization algorithm power distribution control power distribution faults integral squared error load variation OPAL 4510 real-time hardware gorilla troops optimization-based control strategy system objective function voltage control interfaced energy resources Matlab-Simulink simpower-system environment

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Design of beamforming metasurface based on fully connected neural networks 14

Design of beamforming metasurface based on fully connected n...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Li, Shi Sheng, Lili Wen, Jinfang Cao, Weiping Lu, Lucong Guilin Univ Elect Technol Sch Informat & Commun Guilin Peoples R China

ISBN: (纸本)9798350353129;9798350353136

In order to solve the problem that the period of designing metasurface with specific functions is too long by using electromagnetic simulation software, this paper proposes a metasurface design method based on artificial neural network, which can accurately predict the far-field pattern. In the training process, the root-mean-square error decreases with the increase of the number of iterations, the minimum root-mean-square error is 0.01, and the training time is 21 s. After the training is completed, the model can quickly predict the far-field pattern according to the coding matrix. The single prediction time of the neural network is 0.0065 s, while the single simulation time of the full-wave simulation software is 4 min, which is far more than that of the neural network. The accuracy of the predicted data is verified by comparing the predicted results with the results of the full-wave simulation. By combining the neural network with particle swarm optimization algorithm, the metasurface coding matrix corresponding to the target pattern can be designed. Compared with the traditional metasurface design method, this design method greatly reduces the design time and solves the problems of time consuming and cost in the traditional design method.

关键词： Neural network particle swarm optimization algorithm Beamforming metasurface Coding matrix Far-field pattern

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Cooperative Location Method of Multi-UAV Based on TDOA and FDOA

Cooperative Location Method of Multi-UAV Based on TDOA and F...

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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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