Constrained particle swarm optimization for health maintenance in three-mass resonant servo control system with LuGre friction model

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

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A new control system design for a small hydro-power plant based on particle swarm optimization-fuzzy sliding mode controller with Kalman estimator

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331210384533 ◽

2011 ◽

Vol 34 (4) ◽

pp. 388-400 ◽

Cited By ~ 10

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.

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Particle swarm optimization (PSO)-based self tuning proportional, integral, derivative (PID) for bearing navigation control system on quadcopter

2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) ◽

10.1109/icitacee.2017.8257699 ◽

2017 ◽

Cited By ~ 3

Author(s):

Sumardi ◽

Muhammad Surya Sulila ◽

Munawar A Riyadi

Keyword(s):

Particle Swarm Optimization ◽

Control System ◽

Particle Swarm ◽

Proportional Integral Derivative ◽

Swarm Optimization ◽

Proportional Integral ◽

Self Tuning ◽

Navigation Control

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Particle swarm optimization based networked control system design with uncertainty

MATEC Web of Conferences ◽

10.1051/matecconf/201711901051 ◽

2017 ◽

Vol 119 ◽

pp. 01051 ◽

Cited By ~ 1

Author(s):

Ti-Hung Chen ◽

Ming-Feng Yeh ◽

Wen-Yo Lee

Keyword(s):

Particle Swarm Optimization ◽

Control System ◽

System Design ◽

Particle Swarm ◽

Networked Control System ◽

Networked Control ◽

Control System Design ◽

Swarm Optimization

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.1137 ◽

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.

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