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检索条件"主题词=PSO Algorithm"

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A Parameters Identification Method for Hammerstein Systems Based on pso algorithm 27

A Parameters Identification Method for Hammerstein Systems B...

引用

27th Chinese Control and Decision Conference (CCDC)

作者： Wang, Rui Mao, Zhizhong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479970162

The non-linear static components of Hammerstein systems are described by method of polynomial and piecewise linearization method. First, parameters which need to identify through mathematical derivation can be obtained. Then pso algorithm is used to identify Hammerstein system parameters. Computer simulations demonstrate that parameters identification method based on pso algorithm has strong robustness to measuring noise and uncertain models.

关键词： Hammerstein system Parameter identification pso algorithm

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A Novel Strategy for Adjusting Current Pulse Amplitude of FES-Systems with PID based on pso algorithm Method to Control the Muscle Force 14

A Novel Strategy for Adjusting Current Pulse Amplitude of FE...

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14th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Ben Hmed, Abdennacer Bakir, Toufik Garnier, Yoann Binczak, Stephane Sakly, Anis Univ Bourgogne Franche Comte Arts & Metiers CNRS Lab Le2i UMR 6306 Dijon France Univ Monastir Natl Sch Engineers Monastir Res Unit ESIER Monastir Tunisia Univ Bourgogne Franche Comte Lab INSERM UMR 1093 CAPS Dijon France

ISBN: (纸本)9789897583049

Adjusting stimulation parameters using control strategy based on mathematical model, that successfully predict muscle force, may improve the efficiency of Functional Electrical Stimulation (FES) systems. It present an interesting task in industrial FES systems applications. In the present study, we investigate the PID control tuning based on the Particle Swarm Optimization (pso) algorithm at the first time in neuro-muscular systems for updating automatically the stimulation pulse amplitude to track a desired force profiles. In the beginning, The pso algorithm is used to identify unknown force model parameters. Next, according to the identified model, optimal PID gains are found by the same intelligent algorithm. The preliminary obtained results showed promise of using intelligent algorithm on tuning PID to perform control sessions of FES systems.

关键词： Functional Electrical Stimulation(FES) Muscle Force Model PID Controller pso algorithm Pulse Amplitude

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Adaptive Population Differentiation pso algorithm

Adaptive Population Differentiation PSO Algorithm

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3rd International Symposium on Intelligent Information Technology Application

作者： Yang Junjie Xue, Liqin Zhanjiang Normal Univ Sch Informat & Technol Zhanjiang Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9780769538594

In order to solve the problem of easily fall into local optimal solutions, lower convergent precision, slower convergence rates and the poor population diversity, an improved pso algorithm was proposed in this paper. The diversity was improved by the application of fuzzy clustering method. The sub-populations were classified automatically based on the feature of the population, and the information was exchanged by alliance in among the sub-populations. The simulation results of our improved pso and indicated that the performance of optimal precision, efficiency and the stability are much better than that of traditional pso.

关键词： pso algorithm fuzzy clustering diversity

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Digital control method for bucket truck based on pso algorithm 6

Digital control method for bucket truck based on PSO algorit...

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6th International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Jian, Xu Zhang, Liang Sun, Shuangxue Li, Shengzu Wang, Gang Li, Jinbin Huang, Beinuo State Grid Hubei Elect Power Co Ltd Elect Power Res Inst Wuhan Peoples R China Wuhan Power Supply Co Ltd State Grid Hubei Elect Power Co Wuhan Peoples R China China Univ Geosci Sch Automat Wuhan Peoples R China

ISBN: (纸本)9798350307542;9798350307535

In the aspect of power maintenance, the control of bucket trucks has always been a difficult problem. It often requires operators to have enough experience. However, even so, due to the complex environment, it often takes a lot of time to control the end effector of the bucket truck to reach the appropriate position, which greatly reduces the efficiency of the operation and increases the accident rate during the entire process. To solve this problem, this article proposes a digital control method for bucket truck based on pso algorithm. We first established a mathematical model of the bucket truck based on DH parameters and completed its forward kinematics derivation. On this basis, we introduced the pso algorithm to complete the digital control of the bucket truck, solving the problem of high difficulty in obtaining its analytical solution. The experiment shows that the proposed method has high accuracy in both virtual and real environments, greatly improving the control efficiency of the bucket truck.

关键词： power maintenance bucket truck pso algorithm

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A framework for manufacturing features recognition using a Neural network trained by pso algorithm

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IMACS Multiconference on Computational Engineering in Systems Applications (CESA 2006)

作者： Shao, Xinyu Chen, Zhimin Gao, Liang Huazhong Univ Sci & Technol Dept Ind & Mfg Syst Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9787302139225

Recently, the rule-based approach, the graph-based approach, the hint-based approach, the artificial neural networks based approach and the volume decomposition approach are the common feature recognition techniques available today. This work discusses a neural network approach for features recognition from B-rep solid modeler, which has significant effect on improving working efficiency in the product life cycle. pso algorithm is applied to train the neural network. The pso based NN training algorithm can converge faster and more easily achieve a global minimum.

关键词： features recognition pso algorithm Neural network

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ADRC Trajectory Tracking Control Based on pso algorithm for a Quad-Rotor

ADRC Trajectory Tracking Control Based on PSO Algorithm for ...

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8th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Peng, Cheng Tian, Yantao Bai, Yue Gong, Xun Zhao, Changjun Gao, Qingjia Xu, Dongfu Jilin Univ Dept Telecommun Engn Changchun 130023 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun Peoples R China

ISBN: (纸本)9781467363228;9781467363204

A quad-rotor UAV is a typical under-actuated, strong coupled nonlinear system with parameter uncertainty and un-modeled disturbance. For the sake of tracking the desired trajectory, considering these characteristics, an Active Disturbance Rejection Control (ADRC) is introduced in this paper which divides the nested closed-loops control system into four independent channels. ADRC has the capacity of estimating and compensating overall disturbance to improve the system performance significantly. Then Particle Swarm Optimization (pso) is put forward to adjust and optimize parameters of ADRC so as to enhance the system design efficiency. Finally it is demonstrated that ADRC based on pso algorithm has much better performance such as shorter settling time, less overshoot and smaller attitude angle tracking error than classical ADRC in various numerical simulations.

关键词： quad-rotor trajectory tracking ADRC algorithm pso algorithm

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Multiple Models Adaptive Predictive Control based on pso algorithm 16

Multiple Models Adaptive Predictive Control based on PSO Alg...

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16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Liu Gui-ying Qu Li-ping Li Yun-feng Beihua Univ Elect Informat Engn Coll Jilin 132021 Jilin Peoples R China JiLin 524 Factory Jilin 132021 Jilin Peoples R China

ISBN: (纸本)9781538621622

In terms of the characteristics of time lag system, the method of multiple models adaptive predictive control based on particle swarm optimization (pso) algorithm is proposed. Multiple Models can approach the dynamic character of the controlled object. We can design corresponding controller to each model. We can get final controlled variable with the limited controlled variable by means of weighting. Each controller adopts Predictive Control method. At last the method gets the global optimum by pso algorithm. We compare the method to PID in time lag system. Simulation result indicates the method not only can overcome inaccuracy of modeling and time variation of parameters but also has good control performance and stronger robustness.

关键词： pso algorithm Multiple models Predictive control Time lag

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Research on path planning of continuous control UAV based on improved pso algorithm 5

Research on path planning of continuous control UAV based on...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Li, Siyang Liu, Congjian Zheng, Xiaojun Bian, Jinxin Dalian Jiaotong Univ Sch Mech Engn Dalian Peoples R China

ISBN: (纸本)9798350386783;9798350386776

The introduction of intelligent search algorithms provides an excellent solution to the global and local path planning problems of UAVs. The most classic and representative is Particle Swarm Optimization (pso). However, when applying pso algorithm to UAV path planning, it is easy to be constrained by factors such as environmental changes and fail to achieve the expected results. In order to solve the communication delay and communication reliability problems in wireless communication, a path planning study under successive control of UAVs is proposed. In this study, the defects of pso algorithm and the problems in UAV path planning are improved. The improved algorithm introduces the adaptive weight into the update mechanism, and combines the Levy flight strategy to improve the global search strategy to reduce the impact of environmental changes on the algorithm update, so as to ensure the optimal solution. It can be seen from the simulation results that the improved pso algorithm has achieved significant improvement in data update accuracy, local and overall search capabilities. Compared with genetic algorithm, grey wolf optimization algorithm and pso algorithm, the improved pso algorithm has obvious advantages such as fast planning speed and short planning path.

关键词： Consecutive Control Unmanned Air Vehicle pso algorithm Levey Flight Strategy Path Optimization

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Trajectory Planning of Free-floating Space Robot Using an Improved pso algorithm 4

Trajectory Planning of Free-floating Space Robot Using an Im...

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IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Zhu, Zhanxia Zhong, Jianfei Jing, Sa Tang, Biwei Northwestern Polytech Univ Sch Astronaut Xian Shaanxi Peoples R China Natl Key Lab Aerosp Flight Dynam Xian Shaanxi Peoples R China

ISBN: (纸本)9781538653739

In order to ensure non-disturbance on the base attitude of the space robot system during the whole operation process of the robot manipulator, this paper proposes an improved Particle Swarm Optimization(pso) algorithm and uses it to solve the optimal trajectory planning problem of the kinematically redundant space robot in free-floating mode. Firstly, based on the forward kinematics, the joint trajectories are parameterized with polynomial trigonometric function to simplify the calculation. Then, considering the zero-disturbance to the base attitude and the desired pose accuracy of the end-effector, the multi-weighted optimization objective function is established. Thirdly, an improved pso algorithm with self-adaptively updated control parameters is proposed to find the optimal solution of joint trajectories, with which the optimization objective can be satisfied. Finally, the space robot with seven degrees of freedom(DOF) redundant manipulator is employed for simulation. The simulation results demonstrate the effectiveness of the proposed method.

关键词： free-floating space robot(FFSR) trajectory planning pso algorithm

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Optimization Strategy of pso algorithm in Computer Programming and Mathematical Model Construction 3

Optimization Strategy of PSO Algorithm in Computer Programmi...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wen, Xuejuan Yin, Dongmei Changchun Guanghua University Basic Teaching and Research Department Jilin Changchun China

ISBN: (纸本)9798350317022

Traditional mathematical modeling and programming rely on the quality and accuracy of input data. If the data is incorrect or uncertain, it can have a significant impact on the results of the model. pso (Particle Swarm Optimization) algorithm can effectively solve these problems. This study used case analysis and experimental testing methods to analyze the optimization strategies of pso algorithm in computer programming and mathematical model construction. By summarizing existing research and cases, the improved mathematical model performance of the pso algorithm was tested. After research, it was found that the mean square error of the improved mathematical model ranged from 0.01 to 0.05. The optimization strategy of pso algorithm in computer programming and mathematical model construction can significantly improve accuracy, stability of the optimal solution, and mean square error indicators. Parameter tuning is an important strategy to improve the performance of pso algorithms. By optimizing key parameters, global and local search capabilities can be adjusted. Multiple runs and averaging can reduce randomness and noise effects, improve the stability and accuracy of the optimal solution and maintain population diversity to avoid local optima. © 2023 IEEE.

关键词： Computer Programming Mathematical Model Optimization Strategy pso algorithm

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