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 ◽  
...  

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.

Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra

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


2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra

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


2019 ◽  
Vol 8 (03) ◽  
pp. 24491-24501
Author(s):  
Yuwen Pan Zhan Wen ◽  
Yahui Chen, Wenzao Li

Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (RELM) have advantages of fast training speed and good generalization. However, ELM/RELM often needs numerous number of hidden layer nodes to get better performance. The superabundant nodes in hidden layer maybe lead to low running speed. Thus it is not feasible to use ELM in some fields that require high speed algorithms. Therefore, in this paper, we propose an Improved ELM/RELM Optimized based on Chaos Particle Swarm Optimization (CPSO-ELM/RELM) to reduce the number of hidden layer nodes, but still maintain a desirable accuracy. At the same time, it lowers the running speed compared with other algorithms. To verify the application of this method, we design numerous experiments for ELM and RRELM. Their simulation shows that the approach improves the speed of the algorithms, and the accuracy is still high. This makes it possible to use improved CPSO-ELM/RELM in some system with high real-time requirements.


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