Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional–integral–derivative controller tuning of a pressurized water nuclear reactor

2020 ◽  
Vol 136 ◽  
pp. 107019 ◽  
Author(s):  
Seyed Mohammad Hossein Mousakazemi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nuclear Reactor ◽  
Computational Effort ◽  
Proportional Integral Derivative ◽  
Controller Tuning ◽  
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.

PSO-Based Tunning of PID Controller for Speed Control of DC Motor

2014 ◽  
Vol 7 (3) ◽  
pp. 65-79
Author(s):  
Ibrahem S. Fatah
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dc Motor ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Tuning Parameters ◽  
Pid Controller Tuning ◽  
Rising Time

In this paper, a Proportional-Integral-Derivative (PID) controller of DC motor is designed by using particle swarm optimization (PSO) strategy for formative optimal PID controller tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with conventional PID controller using Genetic Algorithm, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the change of the required input do not affect the performances of driving motor with no overtaking.

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