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Study on evaluation of perpendicularity errors with an improved particle swarm optimisation for planar lines

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2013年第1期18卷 54-60页

作者： Zhang, Ke Wang, Shengze Shanghai Inst Technol Sch Mech Engn 100 Haiquan Rd Shanghai Peoples R China Donghua Univ Coll Mech Engn Shanghai Peoples R China

According to characteristics of perpendicularity error evaluation of planar lines, an improved particle swarm optimisation (PSO) is proposed to evaluate the minimum zone error. The evolutional optimum model and the calculation process are introduced in detail. Compared with conventional optimum methods such as simplex search and Powell method, PSO can find the global optimal solution, and the precision of calculating result is very good. Compared to other intelligence computation algorithms such as genetic algorithm (GA), PSO is easier to carry out with fewer parameters to adjust. Then, the objective function calculation approaches for using the particle swarm optimisation algorithm to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and genetic algorithm (GA), indicate that the proposed method can provide better accuracy on perpendicularity error evaluation effectively, and is well suited for position error evaluation.

关键词： metrology perpendicularity error minimum zone solution particle swarm optimisation algorithm

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Research on an optimisation control method of large-scale buildings energy saving based on particle swarm optimisation

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2022年第2-3期44卷 166-181页

作者： Wen, Xiaolong Hunan City Univ Coll Civil Engn Yiyang 413000 Hunan Peoples R China

Aiming at the problems of high energy consumption and low day-lighting coefficient in traditional building energy-saving control methods, an energy-saving optimisation control method for large-scale buildings based on particle swarm optimisation is proposed. Using Autodesk Revit in BIM modelling software the software constructs the large-scale building model, extracts the characteristics of large-scale building organisation information by SIFT method;uses multiple linear regression analysis method to obtain the large-scale building model wall, external window heat transfer coefficient and other parameters, completes the large-scale building operation state analysis;uses particle swarm optimisation algorithm to optimise the large-scale building energy-saving parameters, and obtains its objective function to obtain the large-scale construction Building the optimal energy consumption parameters to achieve large-scale building automation energy-saving control. The experimental results show that: after the energy-saving control of large-scale buildings, the day-lighting coefficient is higher.

关键词： BIM technology large-scale building automation energy saving control multiple linear regression particle swarm optimisation algorithm

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Dynamic modelling of a single-link flexible manipulator system: a particle swarm optimisation approach

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2007年第1期26卷 57-72页

作者： Alam, M. S. Tokhi, M. O. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

This paper presents investigations into modelling of a single-link flexible manipulator system using the particle swarm optimisation (PSO) algorithm. PSO is a population-based search algorithm and is initialised with a population of random solutions, called particles. Basic PSO with best model can hardly provide suitable solutions in the case of real-world multimodal problems. In order to improve diversity in the population set and hence to improve the global searching capability a local version of PSO with time varying inertia and acceleration coefficients is proposed and used in this work. The effectiveness of the algorithm in modelling is validated and verified in terms of tracking, stability and the ability of the derived model in capturing a system's dynamics. The effect of swarm size on the convergence of the proposed PSO algorithm is also analysed. Time domain and frequency domain results of derived models clearly show the potential of the modelling technique and the proposed algorithm in solving such control problems.

关键词： particle swarm optimisation algorithm parametric model single-link flexible manipulator system

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Two redundant rule based algorithms for time-delay nonlinear models: least squares iterative and particle swarm optimisation

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2020年第3期35卷 258-264页

作者： Xu, Yuelin Rong, Yingjiao Sci & Technol Near Surface Detect Lab Wuxi 214028 Jiangsu Peoples R China

Two redundant rule based methods are developed for a time-delay nonlinear model in this paper. By using the redundant rule, the time-delay nonlinear model can be turned into a redundant model which contains some redundant terms. Then the least squares iterative and the particle swarm optimisation algorithms are applied to update the parameters and the corresponding time-delay. Compared with the redundant rule based least squares iterative algorithm, the redundant rule based particle swarm optimisation algorithm is more efficient for nonlinear models with complex structures. A simulation example shows that the proposed algorithms are effective.

关键词： nonlinear model particle swarm optimisation algorithm time-delay parameter estimation redundant rule least squares iterative

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An improved particle swarm optimisation based on cellular automata

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2014年第1期5卷 94-106页

作者： Dai, Yuntao Liu, Liqiang Li, Ying Song, Jingyi Harbin Engn Univ Dept Sci 145 NanTong St Harbin Heilongjiang Peoples R China Harbin Engn Univ Dept Automat Harbin Heilongjiang Peoples R China Anhui Sun Create Elect Co Ltd Dept Image Proc New & High Technol Dev Dist Hefei Anhui Peoples R China Harbin Engn Univ Dept Sci Harbin Heilongjiang Peoples R China

particle swarm optimisation (PSO) algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of particle swarm is considered as cellular automata, and is distributed in two-dimensional grid. The state update of each cell is not only related to its own state and the neighbour state, but also related with the state of the optimal cell. If the state is too close with the optimal cell, then the cell state is re-update. Simulation experiments on typical test functions show that, compared with other algorithms, the proposed algorithm has good robustness, strong local search ability and global optimisation ability, and can solve the optimisation problems effectively.

关键词： particle swarm optimisation algorithm cellular automata function optimisation

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Simplified particle swarm algorithm based on nonlinear decrease extreme disturbance and Cauchy mutation

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2020年第3期35卷 236-245页

作者： Li, Jun Luo, Yangkun Wang, Chong Zeng, Zhigao Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Peoples R China Hunan Univ Technol Coll Comp & Commun Xiangtan Hunan Peoples R China

Concerning the drawbacks that particle swarm optimisation algorithm is easy to fall into the local optima, and has low solution precision, the simplified particle algorithm which based on the nonlinear decrease extreme disturbance and Cauchy mutation is proposed. The algorithm simplifies particle updating formula, and uses logistic chaotic sequence to initialise the particle position, which can improve the global search ability of population;nonlinear decrease extreme disturbance strategy enhanced the diversity of the population and avoid the particles trapping in local optimum;a novel Cauchy mutation is used for the optimal particle variation to generate more optimal guiding particle movement. The experimental simulation on seven typical test functions shows that the proposed algorithm can effectively avoid falling into local optimal solution, the search speed and optimisation accuracy have improved significantly. The algorithm is suitable to solve the function optimisation problem.

关键词： particle swarm optimisation algorithm chaos nonlinear extreme disturbance simplified particle swarm Cauchy mutation

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Optimization of the Dynamic Performance of the Photo-Voltaic and Wind-Turbine Hybrid-Energy System by the particle-swarm Optimization Technique 21

Optimization of the Dynamic Performance of the Photo-Voltaic...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Awad, Hilmy Fathy, Mostafa S. Hegazy, Hosam Elkholy, E. E. Helwan Univ Fac Ind Educ Cairo Egypt Minist Higher Educ Coll Technol Cairo Egypt Menoufia Univ Fac Engn Engn Dept Shibin Al Kawm Al Minufiyah Egypt

ISBN: (纸本)9781728152899

In order to overcome the intermittence of renewable energy sources, such sources are connected in parallel forming hybrid systems. One of these hybrid systems is presented in this paper aiming to investigate and improve its dynamic performance. The hybrid energy system is composed of photovoltaic (PV) arrays and wind turbine (WT) that drives a doubly-fed induction generator (DFIG). Both the PV and WT are linked together through a DC-capacitor link. In order to improve the dynamic performance, a particle-swarm Optimization algorithm is implemented to tune the gains of the applied controllers. The obtained optimum gains are then implemented in a simulation model using the simulink program (MATLAB). Results in the case of optimized gains are compared with initial-design results in the relevant literature. Results show that the dynamic performance of the hybrid energy system with PSO is improved in terms of speed and steady-state error.

关键词： Doubly-Fed Induction Generator (DFIG) wind turbine Maximum Power Point Tracking (MPPT) particle swarm optimisation algorithm Photovoltaic (PV)

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A self-organising cooperative hunting by robotic swarm based on particle swarm optimisation localisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第1期7卷 68-73页

作者： Wang, Zebing Qin, Li Yang, Wei North Univ China Natl Key Lab Elect Measurement Technol Taiyuan 030051 Shanxi Peoples R China North Univ China Sch Mech Engn & Automat Taiyuan 030051 Shanxi Peoples R China

A novel self-organising approach to cooperative hunting by robotic swarm is put forward. Each individual can simply detect the direction angle of moving target. By using particle swarm optimisation (PSO), locating target can be realised through the individual's local interaction. Collective hunting behaviour emerged when human object moved through the detection area. Simulations and experiments demonstrate the feasibility and effectiveness of the proposed approach to cooperative hunting by swarm robotic systems.

关键词： robotic swarm localisation particle swarm optimisation algorithm hunting

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IMPROVED particle swarm algorithm FOR COOPERATIVE MULTI-TASK ALLOCATION OF HETEROGENEOUS UAVs

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MECHATRONIC SYSTEMS AND CONTROL 2023年第1期51卷 42-52页

作者： Lu, Qilin Chen, Yu Qi, Xiaogang Liu, Lifang Xidian Univ Sch Math & Stat Xian 710071 Shaanxi Peoples R China Sci & Technol Near Surface Detect Lab Wuxi Jiangsu Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

This paper proposes a discrete particle swarm optimisation (DPSO) algorithm for solving the heterogeneous unmanned aerial vehicle (UAV) task allocation problem. Such an algorithm takes task priority, resource constraints flight distance, and task revenue into account. First, the specific particle is designed according to the characteristics of the problem, and the corresponding relationship between the allocation plans and the particles is given. A modified strategy is presented for the infeasible particles. On this basis, the original particle swarm algorithm was transformed to a DPSO algorithm. Then, in order to improve the local search ability of particles, an elite operator is introduced on the basis of DPSO, and local search is initiated with a certain probability, forming a new search strategy (IDPSO). Simulation results show that DPSO can be reasonable in solving heterogeneous UAV multi-task problems when the problem size is small. The optimal solution obtained by the proposed IDPSO algorithm is better than the DPSO algorithm, and as the scale of the task allocation problem increases, the superiority of the IDPSO algorithm becomes more significant.

关键词： Heterogeneous unmanned aerial vehicle cooperative multi-task allocation particle swarm optimisation algorithm

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Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第10期14卷 1200-1209页

作者： Wahid, Nurbaiti Zamzuri, Hairi Amer, Noor H. Dwijotomo, Abdurahman Saruchi, Sarah Atifah Mazlan, Saiful Amri Univ Teknol MARA Fac Elect Engn Terengganu 23000 Malaysia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia eMoovit Technol Sdn Bhd Futurise Ctr Prime Lab 3Level 1Cyber 8 Cyberjaya 63000 Selangor Malaysia Univ Pertahanan Nas Malaysia Dept Mech Engn Kuala Lumpur 57000 Malaysia

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

关键词： mobile robots particle swarm optimisation motion control position control collision avoidance adaptive multispeed scheduler adaptive motion planning strategy automated vehicle collision avoidance systems collision speed region motion planner artificial potential field fuzzy system motion planning structure knowledge database information APF parameters particle swarm optimisation algorithm feasible reference motion obstacle positions reference avoidance trajectory data base-type APF maximum reference lateral motion vehicle collision avoidance motion planning strategy

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