Research on the Electrical Control System Building Mode based on Particle Swarm Optimization

2016 ◽  
Author(s):  
Liqiang Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Electrical Control ◽  
System Building

scholarly journals Particle Swarm Optimization Algorithm in Improved Electrical Control System

2021 ◽  
Vol 2066 (1) ◽  
pp. 012020
Author(s):  
Xiaotao Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Control Systems ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Electrical Control

Abstract In today’s social background where high-tech emerges endlessly, various production fields in our country have fully entered the era of mechanical automation and electrical automation, and electrical control systems have been widely used in our country’s electrical appliance manufacturing industry. This paper is based on the theoretical analysis of the particle swarm optimization algorithm. Based on this optimization algorithm, a brand-new particle swarm optimization algorithm is obtained. It is applied to the electrical control system to improve it and makes full use of the improved particle swarm optimization algorithm. The existing electrical control system is optimized. This article firstly analyzes the types of common electrical control systems, puts forward some basic methods to improve the control system, and then explains the effective techniques for improvement, hoping to make reference to the improvement of electrical control systems later in this article. This article first improves the particle swarm optimization algorithm, adding the ability to adjust the control system and dynamic learning factors, focusing on strengthening the later stage of the optimization of the particle swarm algorithm and the ability to converge to improve the efficiency of the calculation. The second is to improve the traditional particle swarm optimization algorithm and update the calculation method of the formula to reduce the possibility of selecting undesirable particles and affecting the optimization results. Finally, through MATLAB and reverse simulation analysis, compared with the traditional electrical control system algorithm, the improved particle swarm optimization algorithm has a faster convergence speed and high control system efficiency. The experimental research results show that the particle swarm optimization algorithm proposed in this paper has a huge advantage compared with other algorithms, and its parameter optimization gives full play to the powerful performance of the electrical 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 new control system design for a small hydro-power plant based on particle swarm optimization-fuzzy sliding mode controller with Kalman estimator

2011 ◽  
Vol 34 (4) ◽  
pp. 388-400 ◽  
Author(s):  
A Zargari ◽  
R Hooshmand ◽  
M Ataei
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Local Network ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Hydro Power ◽  
Fuzzy Sliding Mode ◽  
System Variables

One of the main problems in small hydro-power plants that are locally used is their frequency control system. In this paper, a suggested control system based on the fuzzy sliding mode controller is presented for controlling the network frequency. Also, the proposed control strategy is compared with a PI controller and conventional sliding mode controller. In order to regulate the membership functions of fuzzy system more accurately, the particle swarm optimization algorithm is also applied. Moreover, because of unavailability of the control system variables, an estimator is suggested for estimating and identifying the system variables. This estimator will reduce the costs of implementing the control method. The simulation results show the ability of controller system in controlling the local network frequency in the presence of load and parameter’s variations.

Application of an Improved Internal Model Controller Based on the Particle Swarm Optimization in Network Control System

2013 ◽  
Vol 397-400 ◽  
pp. 1137-1144
Author(s):  
Wei Chen ◽  
Wen Bin Wang ◽  
Zhi Kai Zhao ◽  
Zhi Yuan Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Internal Model ◽  
Particle Swarm ◽  
Internal Model Control ◽  
Network Control ◽  
Step Response ◽  
Network Control System ◽  
Swarm Optimization ◽  
Internal Model Controller

Internal Model Control (IMC) is widely used in Network Control System (NCS) with its strong robustness and simple parameter adjustment. But the accurate dynamic inversion of the IMC model is not easy to find out. To solve this problem, an improved Internal Model Controller is designed with a PID controller and feedback loop, then the Particle Swarm Optimization (PSO) is used to optimize all the parameters of the improved controller. At last, simulation results show that the improved Internal Model Controller can maintain the system stability and the performance of the step response is extremely great in terms of rapidity and anti-interference ability, compared with the classic internal model controller, which enables NCS to achieve a better control effect.

scholarly journals Off-Line Tuning of Positioning Control System using Particle Swarm Optimization Considering Speed Controller

2007 ◽  
Vol 127 (1) ◽  
pp. 52-59 ◽  
Author(s):  
Takayuki Kaneko ◽  
Hiroyuki Matsumoto ◽  
Hironori Mine ◽  
Hideyuki Nishida ◽  
Tomoharu Nakayama
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Positioning Control ◽  
Speed Controller
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