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.

scholarly journals Anti-synchronization of discrete-time chaotic systems using optimization algorithms

2011 ◽  
pp. 143-147
Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Pid Controller ◽  
Chaotic Systems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Modified Particle Swarm Optimization

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.

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.

Double-Loop PID Parameter Tuning for DC Motor Based on Modified Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3139-3142
Author(s):  
Tao Cheng ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
High Performance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameters Optimization ◽  
Double Loop ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

This paper proposes a modified particle swarm optimization to solve identification of tuning PID controller parameters. This paper elaborates the process that MPSO algorithm optimizes PID parameters in double-loop speed control system modeled by simulink. Through analyzing the results of the MPSO optimization, and comparing with standard PSO(SPSO) and traditional method, MPSO algorithm has better dynamic performance, provides a high performance methods for PID parameters optimization.

scholarly journals A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Alrijadjis Alrijadjis
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Modified Pso

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.

scholarly journals PI Controller of Speed Regulation of Brushless DC Motor Based on Particle Swarm Optimization Algorithm with Improved Inertia Weights

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Xie ◽  
Jie-Sheng Wang ◽  
Hai-Bo Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Dc Motor ◽  
Pi Controller ◽  
Brushless Dc Motor ◽  
Swarm Optimization ◽  
Brushless Dc ◽  
Inertia Weight ◽  
Speed Regulation

The brushless director current (DC) motor is a new type of mechatronic motor that has been developed rapidly with the development of power electronics technology and the emergence of new permanent magnet materials. Based on the speed regulation characteristics, speed regulation strategy, and mathematical model of brushless DC motor, a parameter optimization method of proportional-integral (PI) controller on speed regulation for the brushless DC motor based on particle swarm optimization (PSO) algorithm with variable inertia weights is proposed. The parameters of PI controller are optimized by PSO algorithm with five inertia weight adjustment strategies (linear descending inertia weight, linear differential descending inertia weight, incremental-decremented inertia weight, nonlinear descending inertia weight with threshold, and nonlinear descending inertia weight with control factor). The effectiveness of the proposed method is verified by the simulation experiments and the related simulation results.

MODIFIED PARTICLE SWARM OPTIMIZATION FOR AN OPTIMAL FEEDER-SWITCH RELOCATION

2008 ◽  
Vol 17 (02) ◽  
pp. 401-409
Author(s):  
YUTTHAPONG TUPPADUNG ◽  
WERASAK KURUTACH
Keyword(s):  
Particle Swarm Optimization ◽  
Weight Function ◽  
Distribution System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Distribution System ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Weight Method ◽  
Interruption Costs

This paper presents an optimal feeder-switch relocation that is applied by modified Particle Swarm Optimization (MPSO). An inertia weight in Particle Swarm Optimization (PSO) is modified to find the best patterns of distribution configuration. MPSO performance is evaluated by comparison with the conventional inertia weight method. Six different benchmark functions with asymmetric initial range settings are selected as testing functions. A nonlinear inertia weight function is applied in this paper. The results of the experiment illustrate the advantage of MPSO. The optimal feeder-switch relocation in a radial distribution system is used to evaluate the MPSO performance. The results show that MPSO can identify suitable switch locations, based on minimum customer interruption costs.

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