Optimum Setting of DC Drives PID Controller via Particle Swarm Optimization

2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Position Control ◽  
Particle Swarm ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Simulation Results

In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency.To show the validity of the PID-PSO controller, a DC motor position control case is considered and some simulation results are shown. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment.. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.

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.

scholarly journals Optimal parameter values of PID controller for DC motor based on modified particle swarm optimization with adaptive inertia weight

2021 ◽  
Vol 1 (2 (109)) ◽  
pp. 35-45
Author(s):  
Mohammed Obaid Mustafa
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Dc Motor ◽  
Weight Functions ◽  
Swarm Optimization ◽  
Acceleration Coefficient ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Property Control

A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in the second scenario, the four inertia weight functions have been changed but the social and personal acceleration coefficient stayed constant during the algorithm implementation. The comparison between the presented scenarios and traditional PID was carried out and satisfied simulation results have shown that the first scenario has rapid search speeds, and very effective and fast implementation compared to the second scenario and classical PSO and even improved PSO technique. Moreover, the proposed approach has a fast searching speed compared to classical PSO. However, it has been found that the classical PSO algorithm has a premature, inaccurate and local convergence process when solving complex optimization issues. The presented algorithm is proposed to increase the search speed of the original PSO.

Design and Experiment of Iterative Learning Controller Based on Particle Swarm Optimization Approach with New Bounded Constraints Technique

2013 ◽  
Vol 284-287 ◽  
pp. 2233-2237 ◽  
Author(s):  
Yi Cheng Huang ◽  
Yi Hao Li ◽  
Shu Ting Li
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Pid Controller ◽  
Particle Swarm ◽  
Iterative Learning ◽  
Optimization Approach ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Nonminimum Phase ◽  
Motion Profile

This paper utilizes the Improved Particle Swarm Optimization (IPSO) with bounded constraints technique for adjusting the gains of a Proportional-Integral-Derivative (PID) and Iterative Learning Control (ILC) controllers. This study compares the conventional ILC-PID controller with proposed IPSO-ILC-PID controller. A cycloid trajectory for mimicking the real industrial motion profile is applied. Two system plants with nonminimum phase are numerically simulated. Proposed IPSO with bounded constraints technique is evaluated on one axis of linear synchronous motor (LSM) with a PC-based real time controller. Simulations and experiment results show that the proposed controller can reduce the error significantly after two iterations.

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.

SIWPSO-Based Controller Design for AUV

2014 ◽  
Vol 525 ◽  
pp. 736-740
Author(s):  
Jau Woei Perng ◽  
Yi Shyang Huang ◽  
Shiang Shiuan Huang ◽  
Guan Yan Chen ◽  
Chin Yin Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Model ◽  
Pid Controller ◽  
Autonomous Underwater Vehicle ◽  
Controller Design ◽  
Underwater Vehicle ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Linear And Nonlinear ◽  
Simulation Results

A strategy is proposed for a control system with a linearized autonomous underwater vehicle (AUV) dynamic model. The proposed approach combines the particle swarm optimization (PSO) and proportional-integral-derivative (PID) controller to adjust the parameters of the linearized dynamic model. The linear and nonlinear model are both considered in our work. The proposed techniques is verified by using the simulation results to the model of AUV.

scholarly journals Resolving of optimal fractional PID controller for DC motor drive based on anti-windup by invasive weed optimization technique

2019 ◽  
Vol 15 (1) ◽  
pp. 95
Author(s):  
Badriyah Ahmed Obaid ◽  
Ameer Lateef Saleh ◽  
Abbas Kareem Kadhim
Keyword(s):  
Pid Controller ◽  
Optimization Technique ◽  
Design Procedure ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Matlab Simulation ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Simulation Results

This paper exhibits a design procedure for tuning the parameters of Fractional Order Proportional Integral Derivative (FOPID) P  controller to optimize the DC motor drive operation. The optimization technique is establishing on Invasive Weed Optimization (IWO). This paper also proposes the use of anti-windup aspect to against the saturation which may occur in the FOPID controller. The objective of this design is to improve the performance of the drive subjected to different transient response and loading conditions. A comparative study is carried out with a classical PID controller. The Matlab simulation results show more improvements in the proposed system.

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