Steady-state configuration and tension calculations of marine cables under complex currents via separated particle swarm optimization

2014 ◽  
Vol 28 (6) ◽  
pp. 815-828 ◽  
Author(s):  
Xue-song Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Marine Cables ◽  
State Configuration

A Novel Control Scheme for PWM Boost Converter based on Sliding Mode Control using Particle Swarm Optimization

2020 ◽  
Vol 14 ◽  
Author(s):  
Gang Liu ◽  
Dong Qiu ◽  
Xiuru Wang ◽  
Ke Zhang ◽  
Huafeng Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Absolute Error ◽  
Boost Converter ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Steady State Performance

Background: The PWM Boost converter is a strongly nonlinear discrete system, especially when the input voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work. Objective: In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a sliding mode controller of a boost converter. Methods: Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE) indexes by PSO. Results: Simulation verification was performed, and the results show that the controllers tuned by the three indexes all have excellent robust stability. Conclusion: The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state performance.

Performance Analysis for a Gantry Crane System (GCS) Using Priority-Based Fitness Scheme in Binary Particle Swarm Optimization

2014 ◽  
Vol 903 ◽  
pp. 285-290 ◽  
Author(s):  
Hazriq Izzuan Jaafar ◽  
Zaharuddin Mohamed ◽  
Amar Faiz Zainal Abidin ◽  
Zamani Md Sani ◽  
Jasrul Jamani Jamian ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Control Structure ◽  
Lagrange Equation ◽  
Particle Swarm ◽  
New Method ◽  
Gantry Crane ◽  
Binary Particle Swarm Optimization ◽  
Optimal Parameters ◽  
Swarm Optimization

This paper presents development of an optimal PID and PD controllers for controlling the nonlinear Gantry Crane System (GCS). A new method of Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is developed to obtain optimal PID and PD parameters. The optimal parameters are tested on the control structure to examine system responses including trolley displacement and payload oscillation. The dynamic model of GCS is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of the system in terms of settling time, steady state error and overshoot. The result not only confirmed the successes of using new method for GCS, but also shows the new method performs more efficiently compared to the continuous PSO. This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the desired position with low payload oscillation.

scholarly journals Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Hamid Reza Mohammadi ◽  
Ali Akhavan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Steady State ◽  
Equivalent Circuit ◽  
Induction Motor ◽  
Particle Swarm ◽  
Circuit Parameter ◽  
Swarm Optimization ◽  
Full Load

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.

Auto PID Tuning of Speed Control of DC Motor Using Particle Swarm Optimization Based on FPGA

2015 ◽  
Vol 776 ◽  
pp. 390-395 ◽  
Author(s):  
Hilal Tayara ◽  
Deok Jin Lee ◽  
Kil To Chong
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Rise Time ◽  
Particle Swarm ◽  
Speed Control ◽  
Dc Motor ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Pid Tuning

This paper introduces auto tuning of proportional-integral-derivative (PID) controllers of DC motor using particle swarm optimization (PSO) method. The DC motor was modeled in Simulink and PSO was implanted on FPGA “cyclone IV E” using the soft processor NIOS II. The results were efficient in reducing the steady state error, settling time, rise time and maximum overshoot in speed control of a DC motor.

scholarly journals Two-Dimensional Steady-State Boundary Shape Inversion of CGM-SPSO Algorithm on Temperature Information

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Shoubin Wang ◽  
Huangchao Jia ◽  
Xiaogang Sun ◽  
Li Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Optimization Algorithm ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Two Dimensional ◽  
Swarm Optimization

Addressing the problem of two-dimensional steady-state thermal boundary recognition, a hybrid algorithm of conjugate gradient method and social particle swarm optimization (CGM-SPSO) algorithm is proposed. The global search ability of particle swarm optimization algorithm and local search ability of gradient algorithm are effectively combined, which overcomes the shortcoming that the conjugate gradient method tends to converge to the local solution and relies heavily on the initial approximation of the iterative process. The hybrid algorithm also avoids the problem that the particle swarm optimization algorithm requires a large number of iterative steps and a lot of time. The experimental results show that the proposed algorithm is feasible and effective in solving the problem of two-dimensional steady-state thermal boundary shape.

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