scholarly journals PARTICLE SWARM OPTIMIZATION METHOD SOFTWARE ALGORYTHM FOR COMPLEX CONTROL SYSTEM DYNAMIC LINK APPROXIMATION WITH SECOND ORDER APERIODIC LINK

2021 ◽  
pp. 81-87
Author(s):  
D.L. Piotrovskiy ◽  
A.A. Kukolev ◽  
S.A. Podgornyy
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Optimization Method ◽  
Second Order ◽  
System Dynamic ◽  
Swarm Optimization ◽  
Complex Control ◽  
Complex Control System ◽  
Dynamic Link

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

scholarly journals Anti sway tuned control of gantry cranes

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Vladimir A. Suvorov ◽  
Mohammad Reza Bahrami ◽  
Evgeniy E. Akchurin ◽  
Ivan A. Chukalkin ◽  
Stanislav A. Ermakov ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
High Speed ◽  
Low Cost ◽  
Particle Swarm ◽  
Optimization Method ◽  
Angular Speed ◽  
Overhead Crane ◽  
Swarm Optimization ◽  
Speed Regulator

Abstract Load swaying is one of the most frequently occurring problems at production sites. The purpose of this work is to create a control system for the movement of an overhead crane with an anti-sway function. The Particle Swarm Optimization method has been used to find the controller coefficients. The crane movement with the anti-sway function should be implemented using a PLC (programmable logic controller) and have a high speed of operation. The frequency converter controls the speed of the drive that moves the crane. The main advantage of the system is its simplicity and low cost combined with the low swaying of the load. The oscillation amplitude with an angular speed regulator is two to three times less in comparison with the control system without the angular speed regulator. The presence of an angular speed regulator minimizes the impact of the load weight and the rope length. The efficiency of the simulator program for calculating angular speed has been tested and confirmed. Verification of the created mathematical model of the crane with experimental installation has been made. Article Highlights An efficient and low-cost anti-sway system for overhead cranes has been developed. The efficiency of the system was tested experimentally, the dependencies of the influence of factors on the sway angle were obtained. The selection of the regulator coefficients is implemented using the particle swarm optimization method coded in C++, which provides high-speed performance and the ability to integrate the algorithm into the PLC of the overhead crane control system.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

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