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Models of a hybrid wheeled hopping self-balanced robot

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第21期235卷 5776-5795页

作者： Li, Qimin Zeng, Haibing Bai, Long An, Zijian Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Combining wheeled structure with hopping mechanism, this paper purposes a self-balanced hopping robot with hybrid motion pattern. The main actuator which is the cylindrical cam, optimized by particle swarm optimization (PSO), is equipped with the motor to control the hopping motion. Robotic system dynamics model is established and solved by Lagrangian method. After linearization, control characteristics of the system is obtained by classical control theory based on dynamics equations. By applying Adams and Matlab to simulate the system, hopping locomotion and self-balanced capability are validated respectively, and result shows that jump height can reach 750 mm theoretically. Then PID control scheme is developed and specific models of hardware and software are settled down accordingly. Finally, prototype is implemented and series of hopping experiments are conducted, showing that with different projectile angle, prototype can jump 550 mm in height and 460 mm in length, transcending majority of other existing hopping robots.

关键词： Hopping robot self-balancing cylindrical cam Bezier curve particle swarm algorithm

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A time-bounded approach for reducing runtime of well placement optimization using PSO and PSOGA algorithms

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 231卷

作者： Asadian-Pakfar, Mojtaba Sedaee, Behnam Nakhaee, Ali Univ Tehran Inst Petr Engn IPE Coll Engn Dept Chem Engn Tehran Peoples R China

Determining the optimal drilling locations and well production/injection rates is a highly complex decision in reservoir development. The present study aims to optimize well placement and production/injection rates through the application of intelligent search algorithms. The particle swarm Optimization (PSO) and combined particle swarm Optimization-Genetic algorithm (PSOGA) have been chosen as the optimal means to achieve this objective. The Net Present Value (NPV) of the project serves as the objective function, with decision variables including the location of wells, perforation, and flow rates. To evaluate the performance of the algorithms in optimizing well placement and flow rates, a heterogeneous reservoir model has been used. In order to reduce optimization runtime, a novel method has been presented. This method involves changing the termination condition, such that the entire optimization time is considered rather than the maximum iterations. The population size can be added to enhance algorithm convergence. Thus, modified algorithms, namely TPSO and TPSOGA, have been introduced. The results of determining the termination condition on the TPSO-GA algorithm are quite promising. By setting a 24-h termination condition or a 70% reduction in the basic runtime, and with a population size of 30 agents, the same values of the objective function can be obtained. Furthermore, the impact of two parameters within the objective function, namely the oil price and discount rate, has also been explored.

关键词： Optimization Well placement Heterogeneous reservoir particle swarm algorithm Genetic algorithm Reduce computing time

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An novel optimal design method for segmented modification of cycloid gear based on improved transmission efficiency

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2023年第6期17卷

作者： Qiu, Ji Gong, Linhuan Liu, Li Luo, Limin Lou, Junqiang Ningbo Univ Fac Mech Engn & Mech 818 Fenghua Rd Ningbo 315211 Peoples R China Ningbo Zhongda Leader Intelligent Transmiss Co Ltd 185 Zonghan St Ningbo 315301 Peoples R China

In this article, a novel optimal design method for segmented modification of cycloid gear tooth profile was proposed to improve transmission efficiency. Firstly, the cycloid tooth profile under segmented modification was analyzed. Secondly, the mathematical model of the meshing phase angle of the working segment of the cycloid gear with the highest transmission efficiency was established, and such optimization was achieved by the particle swarm algorithm,and the important indexes were calculated and compared with the basic segmented modification method. Finally, the tooth profile of the cycloid gear under the new design method was obtained by numerical solution. To verify the method, the processing technology of cycloid gear is designed, and the comparison of performance testing between the new proposed method and conventional basic segmented modification was conducted and analyzed by the assembled RV reducer prototype in accordingly. The results show the temperature rise of the RV reducer under the optimized method was reduced by 1.2celcius under the rated load, and the torsional stiffness and transmission efficiency were increased by 0.03N center dot m/" and 1.8%, respectively, compared with the basic segmented modification method, which shows that this method can effectively overcome the disadvantage of low transmission efficiency under the basic segmented modification method.

关键词： Cycloid gear Transmission efficiency Segmented modification particle swarm algorithm Processing and manufacturing

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Cutting Parameter Optimization of Stability in Cylindrical Shell Based on particle swarm algorithm

Cutting Parameter Optimization of Stability in Cylindrical S...

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International Conference on Nanotechnology and Precision Engineering (ICNPE 2012)

作者： Chen, Dong Lin, Bin Han, Zhilin Zhang, Yanbin Tianjin Univ Tianjin Key Lab Equipment Design & Mfg Technol Tianjin 300072 Peoples R China

ISBN: (纸本)9783037856406

For cylindrical shell, because of its structure characteristic (i.e. long and thin-walled), it has low stiffness, bad fabrication procedure. And it's very easy to cause vibration in the cutting process, especially the type of regenerative vibration. So, it's vital for the surface quality of work-piece and cutting stability to select cutting parameter reasonably. The paper proposed a dynamic optimization method, which aimed at maximization of material removal rate. This method could guarantee the maximum material removal rate under the condition of cutting stability based on particle swarm algorithm (PSA). In the end, it verified the optimization results by the experiments.

关键词： particle swarm algorithm Cutting stability Parameters optimization Cylindrical shell

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Short-term traffic flow prediction based on improved wavelet neural network

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NEURAL COMPUTING & APPLICATIONS 2021年第14期33卷 8181-8190页

作者： Chen, Qiuxia Song, Ying Zhao, Jianfeng Shenzhen Polytech Sch Comp Engn Shenzhen 518055 Guangdong Peoples R China Wuzhou Univ Sch Business Wuzhou 543002 Guangxi Peoples R China Shenzhen Polytech Sch Automot & Transportat Engn Shenzhen 518055 Guangdong Peoples R China

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO-WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%.

关键词： Short-term traffic flow forecasting Wavelet neural network particle swarm algorithm Improved particle swarm optimization

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Optimized least-squares support vector machine for predicting aero-optic imaging deviation based on chaotic particle swarm optimization

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OPTIK 2020年 206卷 163215-000页

作者： Wu, Yuyang Xue, Wei Xu, Liang Guo, Xiang Xue, Deting Yao, Yuan Zhao, Songbo Li, Ningning Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China Tianjin Univ Technol Sch Elect & Elect Engn Tianjin 300384 Peoples R China Beijing Aerosp Prop Inst Beijing 100076 Peoples R China PICC Property & Casualty Co Ltd Big Data Ctr Beijing 100022 Peoples R China

In the time-averaged flow field, aero-optic imaging deviation (AOID) and its influencing variables are connected by complicated and strongly coupled nonlinear time-dependent relationships that present many difficulties for fast calculation of AOID. To achieve real-time online AOID compensation in practice, chaotic particle swarm optimization (CPSO) and the least-squares support vector machine (LSSVM) are combined to construct a predictive AOID model for supersonic aircraft in flight. With its strong global-search capability, the CPSO algorithm is used to optimize the parameters of the LSSVM predictive model. First, a chaotic sequence is used to initialize the particle positions, thereby enhancing search diversity. The premature convergence of normal particle swarm optimization (PSO) is then countered by using CPSO. If PSO falls into a local optimum, the extreme position of the population is adjusted and the current search trajectory of the particles is disturbed so that the particles search new neighborhoods and paths, thereby increasing the probability of escaping the local optimum. The simulation results show that the predictive accuracy of the CPSO-LSSVM model exceeds that of the LSSVM model alone or the back-propagation neural network model. The CPSO-LSSVM model is effective at compensating for AOID within a limited range, and it avoids the disadvantages of traditional geometrical-optics calculations, namely that they are time-consuming, laborious, and error-prone. When the training data of LSSVM is enough, any AOID can be estimated by the CPSO-LSSVM model.

关键词： Aero-optical effects Image deviation Chaos algorithm particle swarm algorithm LSSVM

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Research on MPPT control strategy of photovoltaic cells under multi-peak

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ENERGY REPORTS 2021年 7卷 283-292页

作者： Zhou, Chun-guang Huang, Liang Ling, Zai-xun Cui, Yi-bo Wuhan Univ Technol Coll Automat Wuhan 430070 Peoples R China Wuhan Complex Dimens Data Technol Co Ltd Wuhan 430070 Peoples R China State Grid Hubei Elect Power Res Inst Wuhan 430077 Peoples R China

Under the condition of partial shadow occlusion, the P-U curve of the photovoltaic array exhibits the characteristics of multipeak output. The traditional maximum power point tracking (MPPT) algorithm is easy to fail, and the particle swarm algorithm, as multi-objective optimization algorithm, is characterized by its low volatility and fast convergence speed. It is applied to the optimization process of multi-peak photovoltaic cells. Aiming at the multi-peak output of the photovoltaic array, this paper builds a simulation model based on the standard particle swarm algorithm and the perturbation and observation method through MATLAB/Simulink. The simulation results show that compared with the traditional perturbation and observation method, the standard particle swarm algorithm can reduce fluctuations when the power is stable and can quickly track the output multi-peak maximum power point when the light intensity changes suddenly. (C) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Maximum power point tracking Perturbation and observation method particle swarm algorithm Multi-peak output

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Clustering algorithm based on improved particle swarm algorithm

Clustering algorithm based on improved particle swarm algori...

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International Forum on Materials Science and Industrial Technology (IFMSIT 2013)

作者： Yang, Jinhui Cao, Xi Baoding Vocat & Tech Coll Modern Educ Technol Dept Baoding City Peoples R China Baoding Vocat & Tech Coll Comp & Informat Engn Dept Baoding City Peoples R China

ISBN: (纸本)9783037858417

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

关键词： cluster analysis K-means algorithm particle swarm algorithm convergence rate globally optimal

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Coordination and Optimization of CCHP Microgrid Group Game Based on the Interaction of Electric and Thermal Energy Considering Conditional Value at Risk

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IEEE ACCESS 2021年 9卷 88664-88673页

作者： Liu Yang Liu Tianyu He Shusen Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China

With the continuous advancement of power market reform, a cluster system composed of multiple microgrids can effectively improve the mutual support of energy between microgrids and improve the stability of system operation. This article comprehensively considers cogeneration, heat pumps, electrical energy storage, and thermal energy storage equipment to establish a framework for a multi-microgrid system. Secondly, due to the heterogeneity of wind power output, load demand and equipment parameters of different microgrids, considering the possibility of collaboration among multiple microgrids, the essence of energy complementarity to increase overall revenue is explained. On this basis, a cooperative game optimization dispatching model for microgrid group of cooling and heating power sources considering energy interaction is proposed. The Shapley value distribution method is used to distribute the surplus value generated by the cooperation. Furthermore, considering the uncertainty of wind power and load in multi-micro grids, a stochastic programming model based on conditional value-at-risk is established. The simulation analysis shows that the model proposed in this paper can effectively improve the surplus power and heat absorption capacity of the microgrid cooperative alliance, while reducing the impact on the power interaction of the main grid. The value of the conditional risk considered is also multi-microgrid. The system provides a reference plan for the trade-off between economic benefits and risk levels.

关键词： Microgrids Games Optimization Resistance heating Power generation Renewable energy sources Distribution networks CCHP microgrid group conditional value at risk cooperative game Shapley value particle swarm algorithm

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particle swarm Based Service Migration Scheme in the Edge Computing Environment

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IEEE ACCESS 2020年 8卷 45596-45606页

作者： Liang, Liang Xiao, Jintao Ren, Zhi Chen, Zhengchuan Jia, Yunjian Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

With the development of Mobile Edge Computing (MEC), it has become a key technology to realize the vision of the Internet of Things. In MEC, users can upload tasks to edge nodes for faster processing speed and lower local energy consumption. However, as the mobility of users and the limited resources of the edge nodes, some edge nodes cannot provide high-quality services. In this case, we study service migration strategy in the MEC system to migrate services from the initial nodes to other edge nodes that can provide services to meet the needs of users. By making service migration decision and allocating computation resource, our work minimizes the delay and the energy consumption caused by finishing tasks. Specifically, we set up an efficient service migration model and formulate the service migration problem as a non-linear 0-1 programming problem. To solve this problem, we design a particle swarm based Service Migration scheme (PSSM) which includes Queuing Delay Prediction algorithm (QDP), Delay-aware Computation Resource Allocation algorithm (DCRA), and Modified Quantum particle swarm algorithm (MQPS). For evaluating the performance of the proposed PSSM, we conduct simulation in a practical scenario. The results demonstrate that our scheme not only can effectively reduce delay and energy consumption, but also improve the processing capability of servers.

关键词： Delays Task analysis Servers Base stations particle swarm optimization Prediction algorithms Virtual machining Mobile edge computing service migration mobility particle swarm algorithm

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