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Force-displacement hysteresis characteristics of PAM based on improved generalised Prandtl-Ishlinskii model

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第7期239卷 2512-2528页

作者： Zhang, Yeming Liu, Chuangchuang Cai, Maolin Shi, Yan Gong, Sanpeng Zhang, Hui Zhang, Shuaijie Henan Polytech Univ Sch Mech & Power Engn Jiaozuo Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Pingyuan Filter Co Ltd Xinxiang Peoples R China

As an advanced actuator that simulates the characteristics of biological muscle, the internal structure and working principle of pneumatic artificial muscle imply significant complexity and highly nonlinear characteristics, which are particularly prominent in the theoretical modelling process, and pose many challenging problems for researchers. The classical Prandtl-Ishlinskii model fails to provide ideal mathematical descriptions and accurate models when dealing with the nonlinear characteristics of pneumatic artificial muscle, resulting in the construction of models for such nonlinear systems that still require further research. Based on the above, this paper proposes a generalised Prandtl-Ishlinskii model based on the improved force-displacement hysteresis characteristics of pneumatic artificial muscle, which employs an improved particle swarm optimisation to identify the unknown parameters of the model, and applies the identified parameters to construct the force-displacement hysteresis curves of pneumatic artificial muscle. Comparing the model fitting results with the experimental data, it is found that the maximum error of the improved generalised Prandtl-Ishlinskii model is reduced by more than 50% and the average error is reduced by more than 75% compared with the classical Prandtl-Ishlinskii model when the internal pressure of the pneumatic artificial muscle is 0.35, 0.4 and 0.45 MPa. The improved generalised Prandtl-Ishlinskii model can better characterise the asymmetric hysteresis property, which improves the model accuracy of the force-displacement coupling of pneumatic artificial muscle, and provides a certain theoretical basis for the later realisation of the precise control of the force-displacement coupling of pneumatic artificial muscle.

关键词： Pneumatic artificial muscle force-displacement hysteresis characteristics improved generalised Prandtl-Ishlinskii model improved particle swarm optimisation algorithm parameter identification

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Passenger inflow control with hierarchical coordination for overloaded metro lines

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第11期14卷 1418-1425页

作者： Zhou, Huijuan Liu, Yu Zhao, Yu North China Univ Technol Beijing Key Lab Urban Intelligent Traff Control T Beijing 100144 Peoples R China CCCC Railway Consultants Grp Co Ltd Beijing 100088 Peoples R China China Acad Railway Sci Corp Ltd Stand & Metrol Res Inst Beijing 100081 Peoples R China

Due to the congestion in urban rail transit during peak hours, this study proposes a hierarchical coordinated passenger inflow control model for overloaded metro lines from three dimensions, namely line, station and ticket gate. The hierarchical structure divides the system into three control layers and the model is established using the linear programming method. An improved particle swarm optimisation (PSO) algorithm, combining the interior penalty function and the basic PSO algorithm, is used to find the optimal objective value. In addition, the objective function with the penalty function to transform the constrained optimisation problem into an unconstrained optimisation problem is developed to solve the model. Finally, a real-world instance with operation data of the Beijing Metro Line 5 is implemented to demonstrate the performance and effectiveness of the proposed approaches.

关键词： particle swarm optimisation linear programming rail traffic control overloaded metro lines urban rail transit peak hours hierarchical coordinated passenger inflow control model ticket gate control layers linear programming method improved particle swarm optimisation algorithm interior penalty function basic PSO algorithm optimal objective value objective function constrained optimisation problem unconstrained optimisation problem Beijing Metro Line 5

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Assessing the effect of wind power peaking characteristics on the maximum penetration level of wind power

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第16期9卷 2466-2473页

作者： Jiang, Xin Chen, Hongkun Xiang, Tieyuan Wuhan Univ Dept Elect Engn Wuhan 430072 Peoples R China

A probabilistic approach with considering the peaking characteristics of wind power is proposed to determine the maximum wind power penetration level (WPPL). To evaluate the effect wind peaking characteristics brought to the hosting capacity of systems, a detailed simulation of wind speed/load distributions over a long period is required. Thus, taking an hour as the time scale, the time-sharing peaking characteristics of wind power are discussed by describing the hourly distribution of wind output and load. Based on the chance constrained programming, a WPPL model is established in this study. The optimisation process is achieved using the improved particle swarm optimisation algorithm based on Monte-Carlo simulation. This proposed approach has been applied to an actual power system in central China. Simulation results show that the WPPL would be too optimistic without considering the anti-peaking characteristics of wind power. Besides various sensitivity analyses are performed to assess the effect of other parameters on the maximum penetration level.

关键词： wind power particle swarm optimisation Monte Carlo methods load distribution wind power plants load distribution wind speed power system Monte Carlo simulation improved particle swarm optimisation algorithm WPPL chance constrained programming time scale maximum wind power penetration level wind power peaking characteristics effect

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Cooperative look-ahead control of vehicle platoon travelling on a road with varying slopes

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第2期13卷 376-384页

作者： Zhai, Chunjie Luo, Fei Liu, Yonggui South China Univ Technol Sch Automat Sci & Engn Wushan 381 Guangzhou Guangdong Peoples R China

In the current literature, little research has been done in the eco-driving of vehicle platoon. To increase the fuel efficiency of vehicle platoon and ensure vehicle safety, this study proposes a switching control strategy for vehicle platoon travelling on a road with varying slopes, where the platooning and eco-driving technologies are used. To improve the fuel efficiency of vehicle platoon, the cooperative look-ahead controller is proposed by formulating a cooperative look-ahead control problem of vehicle platoon based on distributed model predictive control (CLCbDMPC), which is a constrained nonlinear non-convex multi-objective optimisation problem. To solve the CLCbDMPC problem quickly, after the minimum fuel consumption table and its corresponding optimal reduction gear ratio table are constructed and calculated off-line, the improved particle swarm optimisation algorithm with multiple dynamic populations is presented. Furthermore, the safety controller acting as the emergency brake to guarantee vehicle safety is also designed. The switch between the look-ahead controller and the safety controller forms the switching control strategy of vehicle platoon. Simulation results demonstrate, compared with benchmarks, the proposed strategy can significantly save up to 21.88% of fuel for vehicle platoon, and vehicle safety can also be guaranteed.

关键词： road vehicles road traffic control concave programming distributed control predictive control particle swarm optimisation switching systems (control) cooperative look-ahead controller vehicle platoon eco-driving technologies fuel efficiency vehicle safety switching control strategy cooperative look-ahead control problem distributed model predictive control CLCbDMPC problem constrained nonlinear nonconvex multiobjective optimisation problem minimum fuel consumption table optimal reduction gear ratio table improved particle swarm optimisation algorithm multiple dynamic populations safety controller

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Abnormal network data mining model based on deep training learning

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INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY 2020年第4期13卷 228-236页

作者： Jiang, Xiaoling Zhang, Hui Xu, Jiaming Wu, Weicheng Xie, Xingyong Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Humanity Huaian 223003 Peoples R China Huaiyin Inst Technol Off Acad Affairs Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Chem Engn Huaian 223003 Peoples R China

Aiming at the problems of low detection efficiency and poor clustering effect in traditional abnormal network data mining process, an abnormal data mining model based on kernel extreme learning machine and particle swarm optimisation is proposed. The enhanced local linear embedding algorithm is used to extract the features of abnormal network data, and the required feature dimensions are extracted repeatedly to obtain the corresponding features of target network data. K-means algorithm is introduced to cluster the target network data to increase the identification of data mining. By improving the particle swarm optimisation algorithm to optimise the parameters of the kernel limit learning machine, the final abnormal data mining results are the best. The experimental results show that the proposed method has high detection efficiency and good clustering effect, which fully proves the superiority of the proposed method and lays a foundation for the progress of abnormal network data mining technology.

关键词： local linear embedding algorithm target network data characteristics k-means algorithm improved particle swarm optimisation algorithm

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Optimal scheduling model of wind power generation considering the participation of electric vehicle batteries

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The Journal of Engineering 2018年第13期2017卷 1940-1946页

作者： Liang Feng Kuihua Wu Jian Wu Wei Sun Bo Yang Jian Wang Peng An Economic and Technology Research Institute State Grid Shandong Electric Power Company Jinan 250021 Shandong People's Republic of China State Grid Shandong Electric Power Company Jinan 250001 Shandong People's Republic of China State Grid Shandong Electric Power Research Institute Jinan 250002 Shandong People's Republic of China

In order to make full use of the cooperation of the electric vehicle batteries and the wind power generation to realise the peak load shifting, this study mainly investigates the optimisation scheduling strategy of synergistic dispatch between electric vehicle batteries and wind power. Firstly, three synergistic dispatch model including electric vehicles, wind power, and power load demand, and three constraint conditions of the available time, the residual capacity of battery, and the charge–discharge power are proposed by considering the characteristics of transportation and power battery of electric vehicles. This optimisation model is then solved by the improved particle swarm optimisation algorithm. Finally, simulation experiments analyse the factors affecting the synergistic optimisation model of electric vehicles and wind power by considering three kinds of wind power output including normal and peak-regulation and different wind power penetration and the number of electric cars to participate in power grid interaction, and the effectiveness of the synergistic optimisation model to achieve the peak load shifting is confirmed.

关键词： wind power plants power generation scheduling battery powered vehicles automobiles particle swarm optimisation power grids transportation power generation dispatch power grid interaction electric cars wind power penetration normal-regulation peak-regulation synergistic optimisation model improved particle swarm optimisation algorithm transportation charge-discharge power residual capacity constraint conditions power load demand synergistic dispatch model optimisation scheduling strategy peak load shifting electric vehicle batteries wind power generation optimal scheduling model

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Energy storage device locating and sizing based on power electronic transformer

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 3164-3168页

作者： Geng, Qi Hu, Yan Shanghai Jiao Tong Univ Sch Elect & Informat Engn Shanghai Peoples R China

In this study, firstly, the bi-directional energy flow of grid-connected photovoltaic and energy storage system based on power electronic transformer is demonstrated. Based on this, a bi-level programming model is proposed for the location and capacity of energy storage. The optimisation of the location of the outer layer is based on the improved particle swarm algorithm. The energy storage location is a variable, and network loss as well as PET loss are objective functions. The improved particle swarm optimisation algorithm is still adopted to optimise the capacity in the inner layer. The cost of electricity from the main grid is taken as the objective function, and the economic dispatch is realised based on the energy routing strategy of power electronic transformer.

关键词： power grids particle swarm optimisation energy storage photovoltaic power systems power transformers power electronic transformer bi-directional energy flow grid-connected photovoltaic bi-level programming model improved particle swarm algorithm improved particle swarm optimisation algorithm energy routing strategy energy storage device

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