scholarly journals Control of the pressurized water nuclear reactors power using optimized proportional–integral–derivative controller with particle swarm optimization algorithm

2018 ◽  
Vol 50 (6) ◽  
pp. 877-885 ◽  
Author(s):  
Seyed Mohammad Hossein Mousakazemi ◽  
Navid Ayoobian ◽  
Gholam Reza Ansarifar
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Nuclear Reactors ◽  
Particle Swarm ◽  
Proportional Integral Derivative ◽  
Pressurized Water ◽  
Swarm Optimization ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

scholarly journals Vibration suppression of the horizontal flexible plate using proportional–integral–derivative controller tuned by particle swarm optimization

2021 ◽  
pp. 146134842093463
Author(s):  
M Sukri Hadi ◽  
Intan ZM Darus ◽  
Mat H Ab.Talib ◽  
Hanim M Yatim ◽  
M Osman Tokhi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Vibration Suppression ◽  
Particle Swarm ◽  
Flexible Plate ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This paper presents the development of an active vibration control for vibration suppression of the horizontal flexible plate structure using proportional–integral–derivative controller tuned by a conventional method via Ziegler–Nichols and an intelligent method known as particle swarm optimization algorithm. Initially, the experimental rig was designed and fabricated with all edges clamped at the horizontal position of the flexible plate. Data acquisition and instrumentation systems were designed and integrated into the experimental rig to collect input–output vibration data of the flexible plate. The vibration data obtained through experimental study was used to model the system using system identification technique based on auto-regressive with exogenous input structure. The plate system was modeled using particle swarm optimization algorithm and validated using mean squared error, one-step ahead prediction, and correlation tests. The stability of the model was assessed using pole zero diagram stability. The fitness function of particle swarm optimization algorithm is defined as the mean squared error between the measured and estimated output of the horizontal flexible plate system. Next, the developed model was used in the development of an active vibration control for vibration suppression on the horizontal flexible plate system using a proportional–integral–derivative controller. The proportional–integral–derivative gains are optimally determined using two different ways, the conventional method tuned by Ziegler–Nichols tuning rules and the intelligent method tuned by particle swarm optimization algorithm. The performances of developed controllers were assessed and validated. Proportional–integral–derivative-particle swarm optimization controller achieved the highest attenuation value for first mode of vibration by achieving 47.28 dB attenuation as compared to proportional–integral–derivative-Ziegler–Nichols controller which only achieved 34.21 dB attenuation.

Optimal tuning of proportional–integral controller using particle swarm optimization algorithm for control of permanent magnet synchronous generator based wind turbine with tip speed ratio for maximum power point tracking

2020 ◽  
pp. 0309524X2090374
Author(s):  
Marwa Hannachi ◽  
Omessaad Elbeji ◽  
Mouna Benhamed ◽  
Lassaad Sbita
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Speed Ratio ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Point Tracking ◽  
Integral Controller ◽  
Proportional Integral Controller

This article deals with the study of the particle swarm optimization algorithm and its variants. After modeling the global system, a comparative study is carried out about the algorithms described in order to choose the best of those to be used thereafter. Then, the perturbed particle swarm optimization is presented to determine the optimal parameters of the proportional–integral controller for speed control to certify the tip speed ratio for maximum power point tracking of a wind energy conversion system. A numerical simulation is used in conjunction with the particle swarm optimization algorithm to determine the proportional–integral controller optimal parameters. From the simulations results, we observe that the proportional–integral controller designed with particle swarm optimization gives better results compared to the traditional method (proportional–integral manually) in terms of the performance index.

scholarly journals Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

2019 ◽  
Vol 234 (1) ◽  
pp. 39-55
Author(s):  
Ziyang Li ◽  
Quan Zhou ◽  
Yunfan Zhang ◽  
Ji Li ◽  
Hongming Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Knowledge Based ◽  
Accelerated Particle Swarm Optimization ◽  
Fuzzy Knowledge

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.

Optimal torque maximum power point technique for wind turbine: Proportional–integral controller tuning based on particle swarm optimization

2020 ◽  
pp. 0309524X1989290 ◽  
Author(s):  
Marwa Hannachi ◽  
Omessaad Elbeji ◽  
Mouna Benhamed ◽  
Lassaad Sbita
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Integral Controller ◽  
Proportional Integral Controller ◽  
Power Point

This article presents the problem of the energy system optimization for wind generators. The goal of this work is to maximize power extraction for a permanent magnet synchronous generator–based wind turbine with maximum power point technique. This goal is achieved using a proportional–integral controller for optimal torque tuning with the particle swarm optimization algorithm. In order to indicate the effectiveness and superiority of the particle swarm optimization algorithm–based proposal, a comparison with the genetic algorithm and the artificial bee colony algorithm is studied. The system is modeled and tested under MATLAB/Simulink environment. Simulation results validate the advantages of the designed particle swarm optimization–tuned proportional–integral controller compared to P&O and the proportional–integral controller manually in terms of performance index.

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